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May 2015 VOL. 26, NUMBER 5 gpsworld.com

AUTONOMOUS / UAV SPECIAL COVERAGE » COVER STORY AUTONOMOUS FLIGHT

Jammer Hunting with a UAV 30 A fully autonomous, unmanned aerial vehicle (UAV)-based system for locating GPS jammers, currently under development, seeks to localize a jammer to within 30 meters in less than 15 minutes in an area comparable to that of an airport.

Ultimately, the design team targets the ability to locate multiple, simultaneous jammers, and navigate in intermittent GPS and GPS-denied environments using a combination of GPS and alternate navigation aids. The system should be inexpensive and built from commercially available or open-source parts and software.

By James Spicer, Adrien Perkins, Louis Dressel, Mark James, Yu-Hsuan Chen, Sherman Lo , David S. De Lorenzo and Per Enge

INNOVATION

Robustness to Faults for a UAV

40

Integrated Navigation Systems Using Parallel Filtering The authors look at the development of a robust navigation system employing a GNSS receiver, accelerometers, gyroscopes, magnetometers, an airspeed device and dead reckoning to supply a blended navigation solution to a flight control system on a small, unmanned aerial vehicle.

By Trevor Layh and Demoz Gebre-Egziabher

EXPERT ADVICE

Sensor Fusion for Highly Automated Driving

27

By Siamak Akhlaghi

UAV PRODUCT SHOWCASE

39

OPINIONS & DEPARTMENTS

Out in Front

6

THE BUSINESS

Good News for Modern Nav

By Alan Cameron

EXPERT ADVICE

A Leap Second — One More Time!

8

By Dennis McCarthy, Wayne Hanson, Ronald Beard and William Klepczynski

THE SYSTEM

12

Galileo Ground Upgrade; All Systems Go, with a Spring into Space; GPS Glitch Dates from 2011

www.gpsworld.com

14

Furuno Receiver Adopted for Parrot Bebop; NovAtel Launches Relay RTK Radio Module; Phantom 3 Has Indoor Positioning; SBG Systems Selects Septentrio AsteRx4 for Apogee Series; Hemisphere GNSS Offers RTK-Capable Antenna; Applanix Offers Three New Marine Products; Cobham Aeroflex Tester Used for ADS-B; Briefs; Events

ON THE EDGE

Protecting Position in Critical Operations Jamming Signals Criminal Activity in Intermodal Ports

49

By Logan Scott

May 2015 | GPS World

3

ONLINE RESOURCES

NEWSLETTER EXCERPT

All GNSS Attend, But Galileo Gets the Spotlight Let’s give a big hand to Adam and Anastasia, the two Galileo FOC satellites that were successfully launched on March 27. Following the not-sosuccessful Galileo launch in August, it was imperative that this go smoothly.

Although the Double-A launch occurred after the conclusion of this

By Tim Reynolds year’s Munich Satellite Navigation Europe Editor Summit, anticipation of the event set the context for the entire convocation.

The summit is a fixture on the European and global GNSS calendar. It is always intense, often spectacular and sometimes leaves one with contradictory feelings. This year it took place March 24–26 and sought to determine the future of PNT, encouraging delegates to look into the crystal ball and predict developments.

If we go by the number of times these words were repeated during the three days of the summit, the future will hinge around compatibility and interoperability. The multi-constellation GNSS is already here. The elephant in the room remains, as always, interference, but here integration of alternative sensors and signals should hold the key to continuous and possibly resilient operations.

Into the Crystal Ball. Matthias Petschke, director of

EU Satellite Navigation Programmes at the European Commission, admitted that 2014 had been difficult, but he was looking forward to 2015. He expressed confidence that satellite production would remain on schedule. In the long view, he stated: “We will make it for 2020,” signifying full operational capability (FOC).

He also talked about stimulating global markets to foster uptake of Galileo and EGNOS. This was discussed in depth by Carlo des Dorides, executive director of the European GNSS Agency (GSA). The ground infrastructure is very much in place and preparing for the Galileo exploitation phase. A significant milestone in that process would be finding the right partner to lead Galileo operations for the next 10 years. Des Dorides described the process as a competitive dialogue with the emphasis on finding a partner who can inspire new ideas and provide innovative solutions. The contract is big — worth around 1 billion euros. He also emphasized the successes for EGNOS in the year. Almost 180 airports now benefit from EGNOS-enabled approaches and more than 70 percent of “GNSS-enabled” farmers in EU use the EU’s SBAS. One major upcoming trend is a realization that there’s a need to establish a U.S.-wide backup coverage for GPS outage due to natural or man-made interference. The U.S. is currently assessing alternatives with a decision likely in summer 2015.

Read the full column at gpsworld.com/opinions.

Hottest Pages @ GPSWorld.com

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MARKETINSIGHTS WEBINAR

» MAY WEBINAR New Frontiers in Autonomous Flight: Hey You, UAV! Thursday, May 21 10 a.m. PT / 1 p.m. ET / 5 p.m. GMT Speakers: Tony Murfin, GPS World’s contributing editor for Professional OEM; Donald Chance Mark, Jr., an attorney specializing in aviation law; and James Spicer, author of this month’s cover story on the JAGER UAV. Other speakers TBA. Moderator: Alan Cameron, Editor-in-Chief and Publisher, GPS World

Register at www.gpsworld.com/webinar 4

GPS World | May 2015

April 21 – May 20, 2015

1 2 3 4 5 6 7 8 9 10

China Launches First of Next-Gen BeiDou Satellites

LATEST NEWS

Mobile World Congress Sees Rise in Indoor Location Companies (Wireless LBS Pulse)

INSIGHTS

Air Force Working on Glitch for GPS IIF Satellites

LATEST NEWS

All GNSS Attend, But Galileo Gets the Spotlight

INSIGHTS

(EAGER newsletter — see excerpt above)

Quad-Constellation Receiver: GPS, GLONASS, Galileo, BeiDou

FROM THE MAGAZINE

DARPA Ocean Drone Would Lift ‘Upward Falling Payloads’

LATEST NEWS

Javad Ashjaee Urges Diplomacy, Cooperation to End Crimean Stand-Off

LATEST NEWS

Two Galileo Satellites Launched for Europe’s Navigation Constellation

LATEST NEWS

What to Do, Who to See at the 31st Space Symposium INSIGHTS (Defense PNT newsletter)

GPS IIF-9 Successfully Lifts Off from Cape Canaveral

LATEST NEWS

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OUT IN FRONT

Good News for Modern Nav

T

his year’s European Navigation Conference in Bordeaux, France, got underway with “Good news from up there .…”

Galileo’s seventh and eighth satellites launched successfully in late March, the European Space Agency (ESA) plans four more satellites to reach orbit in 2015, and space maneuvers for Galileo 5 and 6 have been completed, with a recovery plan currently under study.

ESA happily confirms that satellites 7 and 8 are in good position, under control, and behaving very well.

Fiammetta Diani, deputy head of Market Development for the European GNSS Agency (GSA), followed her keynote opener with “… some good news also from down here.”

European GNSS (Galileo) Open Service

Ionospheric Correction Algorithm for Galileo Single Frequency Users NAVIGATION SOLUTIONS POWERED BY E U R O P E

© European Union 2015 Document subject to terms of use and disclaimers p.i

The GSA has just published a new document on the NeQuick Ionospheric Model to compensate for ionospheric errors on Galileo and other GNSS signals. The document, “European GNSS (Galileo) Open Service Ionospheric Correction Algorithm for Galileo Single Frequency Users,” downloadable at www.gsc-europa. eu/system/files/galileo_documents/ Galileo_Ionospheric_Model.pdf, contains

detailed description and results from years of research. NeQuick improves 6

GPS World | May 2015

accuracy levels globally when using single-frequency services, even during hyperactive periods of the 11-year solar cycle, according to the GSA. This document gets further discussion in my April GNSS Design & Test e-newsletter column (www.gpsworld.com/DTApril).

The GSA predicts that the installed base of GNSS devices will triple by 2023, with per capita rates of 2.5 in North America, and 2.3 in Europe and Russia. Around the rest of the world, in eight years nearly every person, on average, will possess a GNSS device.

Axelle Pomies of Galileo Services, an association of industry players active in GNSS applications, stressed the need for a comprehensive, assertive industry policy to support the development of EGNOS/Galileo downstream sector, leading to growth, job creation and autonomy for Europe. She previewed the mid-May publication of a draft position paper in this regard, for wide consultation within the European downstream sector. Follow www.galileoservices.org for its first appearance.

Concluding the ENC plenary, Florence Ghiron of Topos Aquitaine, a regional council of satnav and intelligent transport companies in southwest France, focused on opportunities and risks for small-to-medium enterprises.

One of her points: the long development paths of public and regulatory policy do not help SMEs grow.

The Galileo Services and Topos Aquitaine presentations receive more lengthy treatment in my online column mentioned above.

Diani and Ghiron closed with a call to return to Bordeaux in October for the Intelligent Transport Systems World Congress, themed “Towards Intelligent Mobility: Better Use of Space.” GNSS looks to take a more central role than ever in this far-reaching economic segment.

Good news — for us — indeed.

www.gpsworld.com EDITORIAL Editor-in-Chief and Group Publisher Alan Cameron | editor@gpsworld.com Managing Editor Tracy Cozzens | tcozzens@northcoastmedia.net Art Director Charles Park EDITORIAL OFFICES 1360 East 9th St, Suite 1070 Cleveland, OH 44114, USA 847-763-4942 | Fax 847-763-9694 www.gpsworld.com | gpsworld@gpsworld.com CONTRIBUTING EDITORS Innovation Richard Langley | lang@unb.ca Defense PNT Don Jewell | djewell@gpsworld.com European GNSS Tim Reynolds | treynolds@gpsworld.com Professional OEM Tony Murfin | tmurfin@gpsworld.com Geospatial Eric Gakstatter | egakstatter@gpsworld.com GeoIntelligence Art Kalinski | akalinski@gpsworld.com Survey Dave Doyle and Dave Zilkoski Wireless LBS Insider Kevin Dennehy | kdennehy@gpsworld.com Janice Partyka | jpartyka@gpsworld.com BUSINESS International Account Manager Michelle Mitchell | mmitchell@northcoastmedia.net | 216-363-7922 Digital Operations Manager Bethany Chambers | bchambers@northcoastmedia.net | 216-706-3771 Senior Digital Editor Diane Sofranec | dsofranec@northcoastmedia.net | 216-706-3793 Digital Editor Joelle Harms | jharms@northcoastmedia.net | 216-706-3780 Web Developer Jesse Malcmacher | jmalcmacher@northcoastmedia.net | 216-363-7925 Marketing Manager Ryan Bockmuller | rbockmuller@northcoastmedia.net | 216-706-3772 PUBLISHING SERVICES Manager, Production Services Chris Anderson | canderson@northcoastmedia.net Sr. Audience Development Manager Antoinette Sanchez-Perkins | asanchez-perkins@northcoastmedia.net PRODUCTION OFFICE 1360 East 9th St, Suite 1070, Cleveland, OH 44114 216-978-5341 CIRCULATION/SUBSCRIBER SERVICES gpsworld@halldata.com | USA: 847-763-4942 NORTH COAST MEDIA, LLC. President & CEO Kevin Stoltman | kstoltman@northcoastmedia.net | 216-706-3740 Vice President of Finance & Operations Steve Galperin | sgalperin@northcoastmedia.net | 216-706-3705 VP Graphic Design & Production Pete Seltzer | pseltzer@northcoastmedia.net | 216-706-3737

MANUSCRIPTS: GPS World welcomes unsolicited articles but cannot be held responsible for their safekeeping or return. Send to: 1360 East 9th St, Suite 1070, IMG Center, Cleveland, OH 44114, USA. Every precaution is taken to ensure accuracy, but publishers cannot accept responsibility for the accuracy of information supplied herein or for any opinion expressed. REPRINTS: Reprints of all articles are available (500 minimum). Contact 877-652-5295, Nick Iademarco. Wright’s Media, 2407 Timberloch Place, The Woodlands, TX 77380. SUBSCRIBER SERVICES: To subscribe, change your address, and all other services, e-mail gpsworld@halldata.com or call 847-763-4942.

PERMISSIONS: Contact 877-652-5295, Nick Iademarco. Wright’s Media, 2407 Timberloch Place, The Woodlands, TX 77380. INTERNATIONAL LICENSING: Contact e-mail info@gpsworld. com. ACCOUNTING OFFICE and OFFICE OF PUBLICATION: 1360 East 9th St, Suite 1070, IMG Center, Cleveland, OH 44114, USA. GPS WORLD does not verify any claims or other information appearing in any of the advertisements contained in the publication and cannot take any responsibility for any losses or other damages incurred by readers in reliance on such content.

Published monthly

www.gpsworld.com

EXPERT ADVICE

A Leap Second — One More Time!

Dennis McCarthy, Wayne Hanson, Ronald Beard and William Klepczynski

O

nce again we are going to adjust the world’s clocks by one second. This time it will happen on June 30, when we insert another leap second in Coordinated Universal Time (UTC), the standard international time scale. In theory, all UTC clocks should insert a second labeled 23h 59m 60s (the leap second) following one labeled 23h 59m 59s

As the leap second changes take place sporadically, there may be worries that problems could arise because hardware or software may never have been tested thoroughly for a leap second occurrence.

UTC. This is equivalent to having all of the clocks in the world stop for one second at that time.

Are you ready for it?

The last leap second occurred two years ago on June 30, 2012, and the continuation of the process of making these one-second adjustments has stirred a growing controversy over the last few years.

How did the leap second come about — and why do we continue making these sporadic adjustments?

From Sun to Caesium Historically, it has been easy to make use of the apparently uniform repetition of various astronomical phenomena to measure the passage of time. We’re familiar with the Sun rising and setting, and this regularity provides 8

GPS World | May 2015

us a convenient measure of time: the solar day. In recent times until 1960, the average solar day was used as the basis for timekeeping, and if we divide the day into 24 hours, each containing 60 minutes made up of 60 seconds, we can define the second as 1/86,400 of the mean solar day. This meant that the length of the second depended on the Earth’s rate of rotation because it is the

McCarthy

Hanson

Klepczynski

Beard

rotating Earth that causes the Sun to appear to move across the sky.

In the mid-1930s, astronomers concluded that the Earth did not rotate uniformly as measured by the most precise clocks then available. This causes the duration of a second to vary as the Earth’s rotation rate varies. We now know that a variety of physical phenomena affect the Earth’s rotational speed, and consequently this definition of a second became impractical for applications that require a truly uniform time scale. So, in 1960, the second was redefined in terms of the Earth’s yearly orbital motion around the Sun. The time scale provided by this astronomical phenomenon was called Ephemeris Time (ET), to call attention to the fact that its realization depended on the conventionally

adopted positions and motions (that is, the ephemeris) of the Sun (or Moon) that was used in the analyses of the required astronomical observations.

The second defined in this manner was called the Ephemeris second.

Although Ephemeris Time does provide a more uniform measure of the duration of a second, it is inconvenient to make the necessary astronomical observations that would be required to maintain a practical time scale for applications that demand high precision. So, in 1967, the second was redefined again, this time in terms of the frequency of an energy level transition in the Caesium atom, which had already been calibrated with respect to Ephemeris Time by using astronomical observations of the Moon’s motion. Caesium frequency standards, by the early ’60s, had become known as reliable, uniform, accurate and precise clocks. The second defined in this way provided, and continues to provide, a uniform standard of time that can easily be measured in a laboratory with greater precision and accuracy than any astronomical phenomena.

Lab Clocks Rule Although the second defined using the frequency of an atomic energy level transition does provide a unit of time duration that is precise and uniform, it does mean that the passage of time measured in this way is no longer connected to astronomical phenomena. Indeed, with the advent of more accurate observational techniques, astronomers could measure variations in the Earth’s rotation rate by measuring its changing orientation in space and comparing the rate of change with laboratory clocks. They established that among the various variations in the Earth’s rotation rate www.gpsworld.com

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is the gradual slowing down with respect to a uniform atomic time scale. This deceleration is consistent with theoretical tidal effects and observed terrestrial deglaciation.

It is also apparently consistent with ancient observations of solar eclipses, indicating that that this slowing has been going on for thousands of years As a result, if we were to observe a recurring astronomical event, we would see it happening earlier from day to day. To bring our clock back into agreement with the astronomical event, we would have to add some time to the face of our atomic clock.

While astronomers can cope with this situation by applying the appropriate corrections derived from astronomical observations that measure the Earth’s rotation rate, navigators that relied on astronomical observations to determine their positions considered this situation problematic.

When the definition of the second based on the Caesium atom was introduced, it was known that there would be a time varying discrepancy between a clock running at a uniform rate and a theoretical one using a second defined by the Earth’s rotation rate. Starting from 1961, the observed discrepancy was modeled by making small adjustments on the order of a few milliseconds (thousandths of a second) to our clocks at first, and later by making small adjustments to the frequency of the atomic clocks from time to time, usually on an annual basis. This meant that the duration of a second could vary depending on when it was measured.

No More Changes In 1970 the International Radio Consultative Committee (CCIR and now known as the International Telecommunications Union Radiocommunications Sector, or ITU-R) in collaboration with other international agencies adopted a definition of UTC that did away with any periodic changes to the duration 10

GPS World | May 2015

of the second. Instead it was decided that the discrepancy between UTC and the observed rotation angle of the Earth would be accounted for by making one-second adjustments when needed, so that the absolute difference between UTC and the Earth’s rotation angle measured in time units would always be less than 0.9 seconds.

A finer correction would also be provided frequently so that the Earth’s rotation angle in time units designed as Universal Time 1 (UT1) could be derived to 0.1 second precision.

It was specified that the onesecond adjustments, either positive or negative, were to be made preferably at 23h 59m 59s on the last day of the months of December or June, but could also be made, if necessary, at 23h 59m 59s on the last day of the months of March and September, and further if required at 23h 59m 59s on the last day of any month. The implementation of this definition actually began in 1972, a year in which two leap seconds were introduced.

These one-second adjustments came to be known as “leap” seconds by analogy with the “leap” day inserted in calendars. This definition then fixed the second in UTC to be uniformly established as the international standard atomic second defined by the resonance frequency of Caesium and known as the SI (Système International) second.

Compromise Overcome by GNSS The introduction of the concept of the leap second was historically a compromise with practitioners of celestial navigation who needed to base their observations on astronomical time to determine their longitude. If UTC doesn’t differ from the observed rotation angle of the Earth by more than a second, navigators could use UTC directly as a substitute without introducing a systematic error greater than a quarter of a mile. However, the routine practice of using celestial navigation has been overcome by the

success of Global Navigation Satellite Systems (GNSS), inertial navigation systems, and radar navigation.

In fact, the U.S. Naval Academy stopped including celestial navigation in its curriculum in 1998. In the time span since the introduction of the idea of a leap second, computer networks, wireless telecommunication systems, satellite communications, telephone networks, air traffic control systems and even industrial processes have developed to the point where precise time is an essential component of their successful operation. Users and suppliers of these systems are concerned with the impact of sporadic, essentially unpredictable, one-second adjustments.

Most of these modern systems derive their time using GPS timing receivers. Although the navigational solutions make use of GPS System Time, these receivers provide UTC by means of a broadcast correction that provides the time-varying difference between GPS System Time and UTC. This correction normally provides the varying difference between the two times to less than a microsecond but must also keep track of when a leap second is introduced.

As the leap second changes occur sporadically, there may be worries that problems could arise because hardware or software may never have been tested thoroughly for a leap second occurrence. As a result of these concerns, as well as the cost of stopping all of the clocks in the world for one second, the ITU-R has been discussing a possible revision of the definition of UTC by dropping the future use of leap seconds.

Leap or Not Leap?

The question of the future of UTC was raised in 2000 with the suggestion of modifying it to be a continuous timescale without leap seconds.

Consideration of this question is still ongoing. The 2012 World Radiocommunication Conference www.gpsworld.com

EXPERT ADVICE

(WRC-12) identified this issue as urgent, requiring further examination by the 2015 World Radiocommunication Conference (WRC-15) “to consider the feasibility of achieving a continuous reference time-scale, whether by the modification of Coordinated Universal Time (UTC) or some other method, and take appropriate action…”.

With the aim of providing adequate technical background for WRC-15 to make an informed decision on this issue, the International Bureau of Weights and Measures (BIPM) and the ITU agreed to organize jointly a workshop on the future of the international time scale. This workshop was held in Geneva, Switzerland, in September 2013. It provided a unique opportunity to present available information on current and possible future precise frequency and time standards, sources and their characteristics, time scales and dissemination systems and different views on the future of UTC. Contributions to the workshop were specifically invited to ensure that the breadth of the issue would be covered.

Included were the relevant international organizations (the International Astronomical Union, the International Earth Rotation and Reference Systems Service, the International Union of Geodesy and Geophysics, the International Organization for Standardization, the International Maritime Organization, the International Civil Aviation Organization, the Union Radio-scientifique Internationale), the providers of GNSS services (GPS, GLONASS, Galileo and BeiDou), the national metrology institutes that realize and maintain local representations of UTC, the ITU member administrations, and the ITU-T and authorities responsible for electronic time services. Information on the workshop, agenda and presentations is available at www.itu.int/ITU-R/go/itu-bipm-workshop-13.

WRC-15 go into effect. So, leap seconds could be with us for some time yet.

DENNIS MCCARTHY is retired, and serves as a contractor with the U. S. Naval Observatory, where he was science advisor, director of the Directorate of Time, and head of the Earth Orientation Department. Internationally, he has served as president of the Commissions on Time, Commission on Earth Orientation, and Division 1 (Fundamental Astronomy) of the International Astronomical Union (IAU). He was also secretary of Commission 5 of the International Association of Geodesy.

WAYNE HANSON has been a consultant and president of Time Signal Engineering since his retirement in 2001 as chief of the Time and Frequency Services Group in the Time and Frequency Division of the National Institute of Standards and Technology. He is the U.S. chairman of the International Telecommunication Union – Radiocommunication Sector, Working Party 7A concerned with Time Signal and Frequency Standard Emissions.

RON BEARD is the head of the Advanced Space PNT Branch at the Naval Research Laboratory and International Chairman of ITU-R Working Party 7A, Precise Time and Frequency Broadcast Services. During the early development of GPS in the 1970s, he was the project scientist in the NRL GPS Program Office that developed Navigation Technology Satellites One and Two that operated the first atomic clocks in space.

WILLIAM KLEPCZYNSKI is now retired. During his career, he was a consultant to the Institute for Defense Analyses and the head of the Time Service Department of the U.S. Naval Observatory, where he managed the USNO Master Clock, timing operations for GPS and time distribution systems that utilize communications and navigation systems.

Final Decision in November A special issue of ITU News magazine dedicated to the workshop has also been published; an online version is available at https://itunews.itu.int/En/news.aspx?Edition=251. It did not provide a decision on the issues, but rather a forum for issues to be discussed, since there is some controversy over modifying the global reference time scale. The final decision is to be made at the WRC-15 in November when the method for satisfying the feasibility of achieving a continuous time scale will be determined as well as how it would be implemented.

As preparations begin for the June leap second, hardware and software will undergo testing. This process is likely to be repeated for some time to come, even if the decision to eliminate the use of leap seconds in UTC is made. Legacy systems reliant on the use of leap seconds will require an adequate period of time to adapt to any change in the definition of UTC. If the suppression of leap seconds would be decided, it is recommended that a period of time no less than five years be allowed before the Final Acts of the www.gpsworld.com

May 2015 | GPS World

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SYSTEM

THE

Image credit: ESA.

Policy and system news and developments | GPS | Galileo | GLONASS | BeiDou

▲

Galileo Ground Upgrade

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n April 9, the European Space Agency announced completion of a full-scale hardware and software migration to version V2.0 of its global Ground Mission Segment providing all Galileo navigation messages.

The Ground Mission Segment was turned off Jan. 26, allowing the migration to take place over the month of February. March was taken up with detailed checking by operations and system, concluding in a final check on March 31 to validate the successful migration.

“The upgrade has provided better overall performance and availability, along with improved robustness, security and operability,” explained Martin Hollreiser, overseeing mission segment development for ESA, with Thales Alenia Space France as prime contractor. “An overall 25 percent performance improvement is confirmed.

“Three new sensor stations, Kiruna, Ascension and Azores — used to monitor the satellite navigation signals — were added to the operations chain, as well as a new uplink station in Papeete, to uplink corrections incorporated in the navigation message to the satellites for broadcast to the users.”

The Ground Mission Segment at its core is determining the exact satellite orbits and synchronizing all the satellite and terrestrial elements of that clock: the relevant control

12

GPS World | May 2015

THE GALILEO Ground Mission Segment as of March 2013.

center is linked to a global network of ground stations (sensor and uplink stations). The Galileo signals currently undergo technical testing, with early services for the public projected for 2016. “A further update is foreseen for the end of this year,” Hollreiser added, although this will occur with no interruption of services.

GPS Glitch Dates from 2011 On April 15, the U.S. Air Force GPS Directorate said data analysis shows that a technical error affecting some GPS IIF satellites first appeared in 2011. The error affects the way the ground control system builds and uploads messages transmitted by the satellites, but does not affect the accuracy of GPS signals. It involves the groundbased software used to index messages. “A GPS message indexing issue was recently identified that affects a limited number of active GPS IIF satellites, but does not degrade the accuracy of the GPS signal received by users around the globe. The result is an occasional broadcast not in accordance with U.S. technical specifications. ”

More System News Online See www.gpsworld.com.

www.gpsworld.com

THE SYSTEM

All Systems Go, with a Spring into Space

P

lanet Earth gained five new navigation satellites in late March, for four satellite systems.

GPS. The U.S. Air Force’s ninth GPS Block IIF satellite (GPS IIF-9) launched on March 25 from Cape Canaveral, Fla. The IIF-9 rode aboard a Delta IV rocket, the workhorse of the GPS fleet for successful launches. The satellite was declared operational on April 21.

“Many thought the Delta IV and GPS days were long gone, but the recent questions concerning reliable and proven launch vehicles have brought them back online, so to speak,” said GPS World Defense Editor Don Jewell. “The 20-year milestone for GPS space vehicles on orbit that occurred on April 27 translates to approximately 500 orbital years just for the IIR and IIF constellations alone.

The IIAs may account for that many orbital hours as well.

“This is by far the most successful launch record ever put together by any nation or government. No other space-faring nation even comes close.

The U.S. Air Force and all the players should be proud of all these records and more, plus we have one more GPS asset on orbit, providing GPS signals to the world and all they enable, courtesy of the USAF.” Galileo. Two days later, March 27, a duo of Galileo satellites were successfully launched from Europe’s Spaceport in French Guiana. The seventh and eighth Galileo satellites rode aboard a Soyuz ST-B rocket. Both are in their planned orbits.

IRNSS. The next day, March 28, the fourth satellite (IRNSS-1D) of the IRNSS satellite navigation constellation was launched onboard PSLV-C27, and reached its orbital slot April 9. The Polar Satellite Launch Vehicle blasted off from the Satish

www.gpsworld.com

Dhawan Space Center on India’s east coast, in the 28th consecutive successful PSLV mission.

BeiDou. On March 30, China launched the first of a new generation of navigation satellites, BeiDou-3 M1, for its BeiDou constellation. BeiDou-3 M1 is the first of 17 next-generation Beidou navigation satellites. It will have a new navigation signal system with inter-satellite links and other tests to verify the satellite navigation system. The new series of satellites is expected to mark an advancement in the completion of Beidou Phase III several years ahead of schedule, by as soon as 2017 rather than 2020.

GLONASS. Not making the March

launch cut, GLONASS kept its hat in the orbit ring, so to speak, by issuing some far-sighted predictions. Nicholas Testoyedov, CEO of Information Satellite Systems Reshetnev, said that the first GLONASS-K2 spacecraft will be launched into orbit in 2018. “New code division (CDMA) signals will be emitted, so it will provide more accurate positioning for users.”

The GLONASS budget for 2015 will be cut by more than 5 billion rubles, a drop of more than 10 percent.

GLONASS is also suffering through an embezzlement scandal, related to construction of a new ground control center.

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BUSINESS

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» UAVS

Phantom 3 Equipped with Indoor Positioning

▲

Parrot’s quadcopter Bebop Drone has Furuno inside.

Furuno Receiver Adopted for Parrot Bebop Furuno Electric Co.’s latest multi-GNSS receiver module, GN-87, has been adopted for the new quadcopter Bebop Drone, made by Parrot SA. The GN-87 can receive GPS, GLONASS, SBAS and QZSS concurrently, which significantly improves positioning success rate and robustness against interferences by using different frequency bands, Furuno said.

Parrot Bebop Drone, equipped with a 14-megapixel fisheye lens camera, takes video and pictures in a 180-degree field. The drone integrates mechanical and digital systems, like shock absorbers that cushion engine vibrations and algorithms for three-axis image stabilization, meaning that the angle of the view remains fixed without distortion, regardless of the inclination of the drone and movement caused by turbulence. The combination of numerous sensors gives the drone impressive stability and great maneuverability when piloted via Wi-Fi with a smartphone and a tablet, or with its Wi-Fi extender, Parrot Skycontroller.

Furuno’s GN-87 supports sensing for autonomous flying according to flight routes preset on the map application by user, automatic return to takeoff position, and recording flight-path data for 3D modeling on a Parrot Academy map. According to Parrot, selecting Furuno’s GN-87 multi-GNSS receiver module enabled simple integration with a high-performance GNSS receiver module while guaranteeing high quality and high volume supply availability.

DJI’s new Phantom 3 comes in two variations, Professional and Advanced, both of which provide greater control and creative options than the Phantom 2.

Both Phantom 3 versions feature the strongest professional control features DJI has developed so far. Using DJI’s Visual Positioning system, the Phantom 3 can hold its positioning indoors without GPS and can easily take off and land with the push of a button. With Vision Positioning technology, visual and ultrasonic sensors scan the ground beneath the Phantom 3 for patterns, enabling it to identify its position and move accurately.

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DJI held three simultaneous events on April 8 in London, Munich, and New York to mark the release of the Phantom 3.

» SURVEY

NovAtel Launches Relay RTK Radio Module NovAtel’s new Relay RTK radio module is a docking station that provides radio connectivity for its SMART6-L L-band capable GNSS receiver.

The Relay RTK module combined with NovAtel’s SMART6-L receiver creates a compact, easy to integrate positioning solution, NovAtel said.

It is available in four radio versions: 14

GPS World | May 2015

400 MHz UHF licensed band; 900 MHz UHF unlicensed band; HSPA (3G) cellular; and CDMA (1xRTT/

EV-DO) cellular. The CDMA version is approved for use on the Verizon cellular network.

The 400 MHz and 900 MHz versions support both base and rover configurations. The base station is configured via the web-server/Wi-Fi access point using the web browser on a personal computer, tablet or smartphone. The cellular radio versions support reception of NTRIP and RTK corrections over the cellular network. www.gpsworld.com

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THE BUSINESS

» SURVEY & MAPPING

SBG Systems Selects Septentrio AsteRx4 for Apogee Series SBG Systems has selected the Septentrio AsteRx4 OEM GNSS receiver to equip its Apogee product line.

Apogee is a new product line of high-accuracy inertial navigation systems based on robust and cost-effective MEMS technology. The INS/GNSS solution combines the latest generation of MEMS sensors and the OEM version of the AsteRx4, a newly introduced high-precision GNSS receiver from Septentrio. The Apogee series is suitable for applications such as hydrography, mobile mapping and aerial survey where survey-grade positioning measurements are required.

Introduced in April, the AsteRx4 OEM is a multifrequency and multi-constellation dual-antenna receiver

that incorporates the latest innovative GNSS tracking and positioning algorithms from Septentrio. The AsteRx4 is scalable to 1 centimeter and integrates the entire suite of Septentrio’s GNSS+ algorithms to maintain tracking during heavy vibration of machines.

This assures position accuracy under difficult ionosphere conditions and mitigates or rejects intentional or unintentional interference with ▲ SEPTENTRIO’S AsteRX4 OEM GNSS signals.

» MARINE / SURVEY

» MARINE / SURVEY

Hemisphere GNSS Offers RTK-Capable Antenna Applanix Offers Three Hemisphere GNSS is offering a new RTK-enabled Vector V320 GNSS compass. The Vector V320 smart antenna supports multi-frequency GPS, GLONASS, Galileo (future firmware upgrade required) and BeiDou, and Hemisphere GNSS says it’s “the first of its kind.”

Designed for the professional marine and marine survey markets, the Vector V320 is the a multifrequency, multi-GNSS, all-in-one smart antenna capable of both RTKlevel positioning accuracy and better than 0.2-degree heading accuracy in a simple-to-install package.

New Marine Products

The Vector V320 is the latest in a line of GPS/GNSS compasses, including the multi-frequency, multiGNSS Vector VS330 receiver as well as the Vector V102, Vector V103 and Vector V104 compass smart antennas.

» AVIATION

Cobham Aeroflex Tester Used for ADS-B Cobham AvComm, formerly the Aeroflex AvComm business unit, has introduced the ATC-5000NG NextGen ATC/DME Test Set. Designed for engineering development, design validation, manufacturing and return-to-service test applications, the ATC-5000NG is the replacement product for the legacy SDX-2000 and the ATC-1400A/S-1403DL. The software-defined radio architecture supports more transponder RTCA DO-181E test capability than the legacy products did and has new capability needed to support the Federal Aviation Administration’s NextGen test requirements including ADS-B (RTCA DO-260B) and UAT (RTCA DO282). ADS-B is the Automatic Dependent SurveillanceBroadcast for next-generation (NextGen) aircraft navigation.

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GPS World | May 2015

Applanix has expanded its portfolio of marine georeferencing and motion compensation products. The new line-up offers high-performance solutions to a broader cross-section of the hydrographic survey industry.

All Applanix Marine products benefit from the optimal integration of GNSS and inertial observables, with access to Trimble GNSS technology for performance advantages. The entrylevel POS MV SurfMaster incorporates Applanix’s proprietary SmartCal inertial calibration techniques to deliver robust georeferencing for small platforms, both manned and unmanned.

SurfMaster is fully supported by Applanix’ post-processing software POSPac MMS, and can deliver roll and pitch accuracy to 0.03 degrees, regardless of latitude or rate of vessel motion. The POS MV WaveMaster II and POS MV OceanMaster use newly developed inertial technology.

▲

THE POS MV SurfMaster. www.gpsworld.com

FREE WEBINAR Say Goodbye Proprietary GPS Devices, Hello TerraGo Edge THURSDAY, MAY 28 10 A.M. PT / 1 P.M. ET

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Webinar Description Say goodbye to expensive, single-use GPS handheld devices. That’s not the way it works anymore.

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TerraGo Edge replaces all of those single-use GPS handheld devices and enables mobile workers and feld crews to easily collect feld data, share that data with others in real-time using any iOS or Android device, and sync that information with headquarters at a fraction of the cost. With TerraGo Edge, you can save a signifcant amount of money, increase the ROI on your current GIS investments, and improve productivity.

Webinar Benefts Join this webinar and learn how to: ◊ Cut costs by up to 95% ◊ Capture data from any iOS or Android device ◊ Synchronize project updates in real-time ◊ Manage projects, job sites and tasks from a single dashboard ◊ Monitor feld worker locations for safety and efciency

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Alan Cameron

Vice President, Product Management and Marketing, TerraGo Gundling is a dynamic, high-tech product and marketing executive with more than 20 years of experience in launching marketchanging products in the radar, satellite-based navigation, air trafc management and enterprise software industries.

To learn more, visit www.terragotech.com.

Editor-In-Chief and Publisher, GPS World Cameron has worked on GPS World magazine since 2000. He also writes the monthly GNSS System Design e-mail newsletter and the Wide Awake blog. Both these can be found at www.gpsworld.com.

Follow him also on Twitter @gpseditor.

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THE BUSINESS

BUSINESS BRIEFS U.S. Navy to Deploy Underwater Drones from Subs The U.S. Navy plans to deploy underwater drones from submarines later this year. Rear Adm. Joseph Tofalo, director of undersea warfare, said the deployment will include the use of the Remus 600 unmanned underwater vehicles (UUVs) to perform undersea missions around the globe.

response times, Honeywell said. Honeywell Global Tracking is working with the Aerospace & Defense division of Capgemini to deliver a high-precision positioning system compatible with the international Cospas-Sarsat standard.

Telit Module Designed for Harsh Environments Rohde & Schwarz Tests ERA-GLONASS The Certification Center Svyaz-Certificate in Russia is now using the R&S CMW500 to certify ERA-GLONASS systems.

The independent test lab is the only lab in Russia accredited to certify these systems. Effective Jan.1, 2015, all new car models introduced to the Russian market must be equipped with an automatic ERA-GLONASS emergency call system.

Honeywell Passes Galileo Search-and-Rescue Test Honeywell’s Global Tracking solution has passed the final acceptance test for use on the Galileo search and rescue program by demonstrating dramatically reduced emergency

Telit Wireless Solutions’ new positioning module, the SE868-V3, combines GPS, GLONASS, Beidou, Galileo and SBAS, which enables the creation of high-performance position reporting and navigation solutions. It can track GPS and GLONASS or GPS and Beidou constellations simultaneously and is Galileo-ready.

u-blox Module Supports All Satellites u-blox is offering the CAM-M8C, a small, low-profile GNSS positioning module with an integrated wideband chip antenna for reception across the entire L1 band. The module offers simultaneous GNSS operation for GPS/GLONASS, GPS/ BeiDou, or GLONASS/BeiDou.

» EVENTS

For detail, see www.gpsworld.com/events.

6th China Satellite Navigation Conference May 21–23, Xi’an, China; http://182.92.190.247/english/about.asp GEO Business 2015 May 27–28, London; geobusinessshow.com HxGN LIVE: Hexagon’s International Conference June 1–4, Las Vegas; hxgnlive.com TU-Automotive Detroit June 3–4, Novi, Michigan; www.tuauto.com/detroit/

NEWKTEOT! M AR

Joint Navigation Conference June 22–25, Orlando, Florida; http://ion.org/jnc/index.cfm

Industry’s First

Online Buyers Guide Access hundreds of: Manufacturers • Products • Services

IGNSS Society 2015 Symposium & Exhibition July 14–15, Gold Coast, Queensland, Australia; www.ignss.org ION GNSS+ 2015 September 14–18, Tampa, Florida; http://ion.org/gnss/index.cfm

Visit & bookmark it today!

GPSWORLDBUYERSGUIDE.COM Have a question about the directory Contact Chloe Kalin at 216-363-7929.

18

GPS World | May 2015

International Symposium on GNSS 2015 November16-19, Kyoto, Japan; www.isgnss2015.org www.gpsworld.com

EXPERT ADVICE

Sensor Fusion for Highly Automated Driving High-Precision GNSS Needs Help for Continuous Localization Reliability Siamak Akhlaghi

A

GNSS technology alone has limitations that must be overcome to make it suitable for use in autonomous systems. systems. For instance, GNSS signals may become blocked or lost due to: obstructions such as in urban canyon or tunnels; multipath, where signals are reflected off the vehicle body; or signal interference from other RF signal sources.

GNSS correction data and data from other sensors on the vehicle can be used to improve the accuracy and reliability of the vehicle localization solution both globally and with respect to the local environment. To achieve the localization performance, accuracy and integrity required for autonomous vehicles, a multisystem, sensor fusion approach seems to be the most promising. Localization systems will require absolute positioning references like precision GNSS as well www.gpsworld.com

Image courtesy of NovAtel.

utomotive safety and comfort functions, known as Advanced Driver Assistance Systems (ADAS), have become an essential part of modern vehicles.

These functions assist drivers in the driving process, providing capabilities such as adaptive cruise control or highway driving mode. To achieve a desired level of performance, the position of the vehicle must be known.

Precise positioning supports the vehicle’s systems with planning, executing and monitoring of a particular maneuver.

Position determination, or localization, is the estimation of the location, heading, velocity and acceleration of a vehicle with respect to a fixed coordinate system.

High-precision GNSS provides an excellent, worldwide, absolute position reference for localization. However, GNSS technology alone has limitations that must be overcome to make it suitable for use in autonomous as local or relative positioning inputs from inertial sensors, odometers, radar, LiDAR, cameras, infrared and ultrasound sensors. It is clear that no single technology will make highly automated driving possible. Rather, the fusion of the entire vehicle’s sensing technologies will provide the localization accuracy and reliability required.

Achieving Accuracy and Reliability with GNSS GNSS has revolutionized localization in many applications, from precision survey to agricultural guidance. For autonomous driving applications, localization accuracy of 30 centimeters (cm) or less is required. The single-frequency, auto-grade GNSS

Digital Maps Inertial Motion

Radar/ LIDAR

Absolute Positioning

Augmented GNSS

Relative Positioning Camera

Ultrasonic

Infrared

▲

FIGURE 1 High precision / localization with sensor fusion.

May 2015 | GPS World

27

EXPERT ADVICE

GNSS-inertial solution is that the low frequency errors inherent to inertial sensors can be compensated for and removed from the solution.

As a result, sensor fusion algorithms provide a highly robust and stable localization solution at data rates as high as 200 Hz. Other sensors in the vehicle, such as odometers, cameras or LiDAR, can also give information about the relative motion of the vehicle and can add to the redundancy, reliability and stability of the localization solution.

â–˛

FIGURE 2 With a tightly coupled GNSS-inertial solution, low-frequency errors can be removed from the localization solution. The brown dots are the GNSS solution, the blue dots are the inertial solution, and the combined colors represent the tightly coupled solution.

receivers that have been used in vehicles up to now cannot achieve this level of accuracy. Multifrequency GNSS receivers utilizing Precise Point Positioning (PPP) correction techniques can achieve accuracies better than 10 cm. PPP algorithms combine GNSS satellite clock and orbit correction data from a global reference station network

â–˛

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HIGH-PRECISION antennas are key.

GPS World | May 2015

with high precision GNSS receiver satellite observations to yield robust sub-decimeter positioning without the need for local base stations. Since the PPP corrections can be delivered via satellite, the solution is ideal for highly automated driving where communications infrastructure is costly and in some areas may not be available. Recent advances in PPP techniques provide robust positioning and the ability to quickly regain full accuracy following a temporary loss of GNSS signals, for instance under foliage or highway overpasses.

Sensor Fusion Occasional instantaneous irregularities and temporary outages of GNSS can be compensated for by incorporating measurements of the vehicle motion from inertial sensors mounted in the vehicle.

An advantage of a tightly coupled

High-Precision GNSS Antenna Antennas play a critical role in achieving precise localization with GNSS. While GNSS antenna requirements differ depending on the application, ideally the antenna should receive only signals above the horizon, have a known and stable phase center that is colocated with the geometrical center of the antenna, and have perfect circular polarization characteristics to maximize the reception of the incoming signals. Highly automated driving applications demand high performance as well as compact size and strong interference rejection.

Achieving the required performance amidst these challenging constraints will require innovative new GNSS antenna designs.

Autonomous driving will be a reality in the not-too-distant future.

Innovation in the suite of sensors and fusion algorithms used for solving the localization challenge will be paramount to making safe and reliable autonomous vehicles.

Further, innovation developed for automotive autonomy will support new autonomous vehicle applications in other segments.

SIAMAK AKHLAGHI is Segment Manager for Autonomous Systems at NovAtel. He has 20 years of professional experience working for high-tech sectors with broad experience in inertial sensors and navigation systems. www.gpsworld.com

ION GNSS+ 2015 GNSS + Other Sensors in Today’s Marketplace The 28th International Technical Meeting of the Satellite Division of The Institute of Navigation

September 14 – 18, 2015 Tutorials: Sept. 14 – 15 Tampa Convention Center / Tampa, Florida SYSTEMS AND APPLICATION TRACKS Mass-Market Applications High Performance & Safety-Critical Applications System Updates, Plans and Policies

PEER-REVIEWED TRACKS Multisensor Navigation and Applications Algorithms and Methods Advanced GNSS Technologies

And, featuring the popular Indoor Location Panel and Demonstrations

The world’s largest technical meeting and showcase of GNSS technology, products and services.

» COVER STORY

Jammer Hunting with a UAV A fully autonomous, unmanned aerial vehicle (UAV)-based system for locating GPS jammers, currently under development, seeks to localize a jammer to within 30 meters in less than 15 minutes in an area comparable to that of an airport. Ultimately, the design team targets the ability to locate multiple, simultaneous jammers, and navigate in intermittent GPS and GPS-denied environments using a combination of GPS and alternate navigation aids. The system should be inexpensive and built from commercially available or open-source parts and software.

James Spicer, Adrien Perkins, Louis Dressel, Mark James, Yu-Hsuan Chen, Sherman Lo , David S. De Lorenzo and Per Enge, Stanford University

T

he aviation community worries about GPS jamming.

Recently, it struggled to find so-called personal privacy devices on Newark’s Liberty International Airport and traveling the nearby New Jersey Turnpike.

A number of unintentional jamming incidents took a long time to resolve. The disruption from an intentional, malicious jamming attack could be far worse. Airport authorities should be prepared to locate and shut down a coordinated attack by numerous jammers capable of disrupting the GPS service over an entire airport.

The closure of a major airport for the many hours or days it would take to locate even a couple of backpack-sized transmitters would be not only be highly disruptive in flights delayed or diverted, it would negatively impact the confidence of the flying public.

Any system in place to mitigate this threat must be inexpensive enough to be deployed at least at the nation’s

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GPS World | May 2015

major commercial airports, autonomous enough to be operable with limited training and certification, and rapid and accurate enough that a jammer can be routinely apprehended by ground-based law enforcement. It must be able to navigate successfully in GPS-denied environments using alternative position, navigation and timing (APNT), and have the range and capacity to search an airport-sized area as well as the approach corridor leading to runway touchdown.

This article describes such a system and device presently in research and development: the Jammer Acquisition with GPS Exploration & Reconnaissance (JAGER).

Vehicle Design and Operation The JAGER UAV is a based on a commercially available, multi-rotor airframe modified to suit the mission specifications. The 1.2-meter diameter octocopter has a maximum takeoff weight of 11 kilograms (24.2 pounds), a www.gpsworld.com

Security & Surveillance |

top speed of 20 meters/second (m/s, 45 mph), and can fly unloaded for up to 30 minutes.

We have replaced the battery tray with our own carbon fiber design that allows us to carry 16 Ah of lithium polymer batteries for a maximum power draw of 4 kW. This extra capacity means that even with a 5-kilo experimental payload, the present craft can remain aloft for up to 15 minutes without recharging.

The payload plates are also custom-made from carbon fiber, and it is to these that the UAV’s experimental payloads are mounted (see FIGURES 1 and 2). One payload plate is flown at a time, and is secured on top of the airframe with a quickrelease mechanism. This modularity allows for individual experiments to be mounted to their own payload plate and ground-tested before being secured to the UAV. Different experiments can be switched out rapidly for efficient use of battery capacity and flight time.

The plate itself also offers flexibility for component mounting. Regularly spaced, threaded holes across the plate mean components’ positions can be easily changed to find an optimal configuration. This can be particularly useful for minimizing interference between computers and noisesensitive components such as antennas and magnetometers.

Software. We modified existing, open-source autopilot software to fly the mission. The craft is fully capable of completing a mission autonomously, but also can be taken over by a human pilot if necessary. A ground station also can be used to send commands to the octocopter, but is primarily used to monitor UAV location, battery life, and jammer belief state.

The autopilot software also has been adapted to communicate with various vehicle payloads. Experiments using APNT equipment, for example, pass their data to the autopilot, which will combine these signals with its own GPS data for accurate navigation in areas where the GPS signal might be intermittent or unreliable. In return, the autopilot can be used to pass data to experiments reliant on altitude, attitude, atmospheric pressure or location information.

The ground station monitors instruments’ data and status in real time. This not only allows for control of airborne experiments, but also straightforward ground testing.

Synthetic autopilot data can be fed to an experiment to ensure that all systems are performing correctly before they are mounted on the vehicle for flight tests.

APNT Overview Key to navigating in a GPS-denied environment is the use of signals from APNT networks for location determination.

The proposed system should be able to navigate using any or all available APNT signals, and should weight each one according to its strength and reliability in order to formulate the most accurate estimate of both its own and the jammer’s position.

Here we describe the use of the universal access transceiver www.gpsworld.com

AUTONOMOUS FLIGHT

▲

FIGURE 1 (A) Diagram of the payload plate showing regularly spaced mounting holes. (B) Plate with APNT experiment mounted. (C) Payload plate / experiment assembly secured atop JAGER UAV.

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FIGURE 2 Image of the vehicle showing the battery tray slung beneath the central body, the APNT experiment and payload plate secured on top, and the jammer-hunting antenna mounted at the front.

(UAT) and distance measuring equipment (DME) network for our APNT signals. The UAT signal has been implemented by the Federal Aviation Administration (FAA) in the United States as part of automatic dependent surveillance–broadcast (ADS-B), and is transmitted through a network of terrestrial ground stations.

The ADS-B network was only completed across the contiguous United States in 2014, so it is new compared to established cellphone networks. It is more comprehensive than many other terrestrial systems, so that coverage of most airports is guaranteed. While GPS reception requires an unobstructed view of the sky, UAT reception requires a direct line of sight to a transmitting tower. However, the flatness of terrain surrounding most airports as well as the UAV’s airborne vantage point ensures that UAT signals will probably be visible throughout most jammer-seeking missions.

The APNT equipment used for navigation by the JAGER UAV consists of UAT (978 MHz), DME (982 to 1213 MHz), and GPS (1575.4 MHz) antennas, a multichannel transceiver to combine the two signals, and a computer for data processing (see FIGURE 3). A dedicated lithium-ion battery powers the entire APNT payload. The current system May 2015 | GPS World

31

AUTONOMOUS FLIGHT | Security & Surveillance

does incorporate GPS to estimate the time offset, but future iterations of the system will derive time from sources other than GNSS so that true GPS-denied navigation is possible.

The UAT antenna receives multiple signals from visible ADS-B ground station transmitters. The transceiver combines these with a GPS timestamp, and the data is passed to the APNT computer for analysis. Based on knowledge of the absolute locations of the ADS-B antennas, the range of the vehicle from each antenna can be calculated, which in turn can be used to trilaterate the vehicle’s absolute position.

This position is then passed to the autopilot for the UAV’s navigation, while the status of the equipment and signal strength are passed down to the ground for monitoring in realtime.

The necessity of using GPS signals as an accurate timing system is a current limitation, as navigation in GPS-denied conditions is clearly not possible while we are using GPS as a clock. As mentioned eariler, future designs will derive time from non-GNSS sources, such as chip-scale atomic clocks or the terrestrial ranging signals.

Carrying an onboard computer allows for real-time processing of the terrestrial alternative navigation signals.

However, there are a few limitations to the use of these signals. First, the vertical position is difficult to calculate due to the geometry of terrestrial signals as well as the sparsity of visible station at low elevation. This is solved by using a baro-altimeter. Second, DME signals do not provide a pseudoranging function. Current work sponsored by the FAA is developing a DME pseudoranging capability. As the technology matures, we will improve the hardware and algorithm that can be integrated into future JAGER designs, resulting in lower weight and power overhead for the APNT payload.

Tracking Overview GPS jammers do little more than emit signals in the GPS frequency range. Because the signals from GPS satellites are so weak by the time they reach the Earth, ground-based jammers do not have to be especially powerful to overwhelm GPS in their immediate vicinity. A jammer is no more than a ground-based radio-frequency source radiating within the GPS spectrum.

The JAGER system will autonomously locate the nearest beacon emitting electromagnetic signals at the target frequency: the GPS frequency in this scenario. Testing such a system is difficult due to the illegality of jamming the GPS signal within the United States. We instead test the system using a powerful Wi-Fi beacon as a proxy for the overpowering jammer. Excepting the target frequency, the procedure to locate the jammer is identical to the GPS case.

To receive the jamming signal, the front of the craft carries an antenna optimized to receive signals of the target wavelength; the current antenna has a 60° cone of maximum 32

GPS World | May 2015

▲

FIGURE 3 Schematic of the APNT configuration on board the JAGER UAV. Resulting location information is passed to the autopilot for navigation.

▲

FIGURE 4 Schematic of the tracking system on board the JAGER UAV. The resulting believed location of the target is passed to the autopilot.

sensitivity. It is angled downward 30° from the horizontal, so that the craft can receive all signals from the horizon to 30° from vertical. This gives the UAV visibility over most of the space in front and underneath it. Like the other payload equipment on the vehicle, the antenna is secured with a fastrelease mechanism so that it can be easily swapped out if necessary. For Wi-Fi tracking, we use a Yagi antenna with 60° beamwidth and 9 dBi gain. In upcoming trials, we will test different antenna configurations (such as dual antennas, small antenna arrays, and directional antennas augmented with omni-directional antennas) to determine benefits of these different layouts.

Signals from the antenna are passed into a module that converts the Wi-Fi data to serial, then from serial to USB. A single-board Linux computer with a quad-core processor then analyzes the signal data (see FIGURE 4). The hardware used to locate the jammer weighs 160 grams, so has negligible impact on the vehicle’s flight time or range.

To find the jammer’s location, the UAV performs a controlled yaw spin while recording the strength of the jamming signal. On the basis of the signal landscape surrounding the vehicle, the computer estimates the jammer’s location and sends a message to the autopilot instructing the craft to fly in that direction (or, more accurately, in a direction that optimally improves the ability of JAGER to find the jammer quickly). In return, the autopilot updates the tracking computer and ground station as to the vehicle’s position. www.gpsworld.com

Security & Surveillance |

After moving a certain distance towards the jammer’s believed location, the craft repeats the spinning maneuver and starts the process again. Although rotating only the antenna might increase the speed of the operation, the energy required to carry the necessary antennarotation mechanisms for the duration of a flight is more than that needed to spin the entire craft.

The tracking algorithm is not as straightforward as gradient ascent or homing, and the vehicle will not always fly in the direction of greatest signal strength. The operational area is uneven, and may include buildings, towers, or airplanes, resulting in a complicated RF environment. Signals are scattered, diffracted and reflected, meaning that an algorithm that simply follows the strongest signal will not always converge on the actual jammer location.

To decide the optimal path from the vehicle’s present location to the jammer’s believed position, the tracking algorithm makes use of partially observable Markov decision processes (POMDPs). POMDPs model decision processes where the underlying state of the system (that is, the location of the jammer) is never completely known, and maintain a probability distribution over the set of all possible states.

The entire deployment area (an airport and its environs, for example) is split up into a square grid. For every possible combination of jammer and vehicle grid square locations, the signal strength and direction that would result is calculated offline prior to deployment and stored in a database on the tracking computer.

During the mission, the UAV records its own position and the sensed jamming signal’s strength and direction.

The jammer location that would correspond to this result is retrieved from the database, as well as a measure of the strength of this belief state.

Once the craft has a belief as to the location of the jammer, it moves to a new location in the jammer’s believed direction before taking another www.gpsworld.com

measurement of signal strength. The new location and new measurement are combined, and the updated corresponding jammer location is retrieved from the database. This process is repeated until the vehicle believes itself to be right above the

AUTONOMOUS FLIGHT

jammer, at which point a photograph is taken, the ground station is notified, and the hunting mission is complete.

Having found the jammer, the system can be programmed to execute a wide range of operations. These include reporting coordinates and a live image

May 2015 | GPS World

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AUTONOMOUS FLIGHT | Security & Surveillance

▲

FIGURE 5 UAT range deviates from GPS derived range-estimate by an average of only 16.6 meters throughout the duration of the test flight.

of the believed jammer location back to the ground station, hovering above and tracking the jammer if it begins to move, landing at the jammer site, or returning to base.

We calculate and store the POMDP decisions in advance of the flight. This strategy has some advantages. First, it allows for almost instantaneous decision-making. This is because the algorithm’s decisions are based solely on the vehicle’s current location and sensory observations and not on any previous states (a defining characteristic of a Markov decision process).

The craft needs only to observe its current state in order to look up its next move in the database. This enables rapid tracking in flight.

A second advantage is that safety checks can be preprogrammed into the database in advance of deployment.

While JAGER is programmed to move towards the grid square believed to contain the jammer, it can also be programmed to avoid or take special precautions when moving towards or in the vicinity of certain squares in the grid (also called geo-fencing). In an airport situation, for example, the vehicle would avoid moving into the square containing a control tower or ground-based antenna, or would fly at a minimum altitude over buildings and taxiways to avoid collisions.

Finally, the integration between the autopilot and the tracking software can provide other important safeguards: in the proof-of-concept system, any navigation decision taken by the software can be relayed to the ground for human verification before the UAV begins to move. This supervised mode of operation lends itself to a seamless migration path to fully autonomous operation (always overseen by a human operator).

However, one disadvantage of calculating and storing decisions in advance is the storage space needed on the vehicle. Because the result of every possible combination of vehicle and jammer locations within the grid is calculated, the 34

GPS World | May 2015

size of the database grows quickly with increasing numbers of possible positions (and states). The larger the grid or the greater the required accuracy, the more space is needed to store the database. With current algorithms, the database needed to locate a jammer to within 30 meters in an area the size of an airport requires 15 gigabytes of storage space, resulting in longer lookup times during flight.

We are considering several strategies to mitigate this disadvantage, including better compression, more effective search algorithms, and uploading from a ground server only the parts of the database that correspond to the vehicle’s current operational area. Another strategy is to use an adaptive mesh that changes in resolution depending on the jammer’s belief state: at low certainty the database resolution is low, but increases in the appropriate area as the jammer’s location becomes more certain.

Another disadvantage of pre-solving the decision-making process is that the system must be reconfigured for every site in which it is deployed. The specifications of the tracking algorithm will change depending on the requirements of the operating area. The grid size, shape and absolute location must change to suit the area being protected. The resolution of the grid depends on the required accuracy of the tracking system, and restricted or prohibited locations must suit the terrain, buildings and geological features of the deployment space. For example, a lead JAGER vehicle could be adapted and tested to suit a particular airport, and then the bespoke algorithm and database uploaded to backup vehicles in that airport’s fleet.

APNT Performance During the Joint Interagency Field Experimentation (JIFX) event at Camp Roberts, California, in November 2014, we tested the APNT system by deploying the vehicle with GPS, UAT and DME antennas simultaneously recording data.

GPS receivers on the ground were used to collect reference measurements to estimate the time of transmission of the signals from the APNT sites. All signals were recorded at an altitude of 275 meters above ground level (600 meters above sea level), at four different points roughly 800 meters apart, and the data analyzed for comparison. As expected, the UAT broadcast was noisier than the GPS signal. However, it was possible to calculate a range from the UAT data that was accurate to within 16.6 meters of the GPS reference position, well within the 30 meters error bound specified in the project specification (see FIGURE 5).

While UAV navigation using APNT was done offline in post-processing for these tests, with planned algorithm improvements and hardware acceleration the UAT signal can be used to get real-time position information nearly as accurate as that from GPS. Thus the JAGER UAV can be navigated with comparable reliability in both GPS and GPSdenied environments.

Terrestrial APNT signals will be received at a wide range of power levels. This effect is not observed with the GPS www.gpsworld.com

Security & Surveillance |

AUTONOMOUS FLIGHT

network, as the different satellite signals are broadcast from such a great distance that any differences in received signal strength are relatively small by the time they reach Earth.

For terrestrial networks, signals from transmitters close to the receiver can be many times stronger than those further away, which can result in two issues: 1) interference where one signal overwhelms another, and 2) inability to process a signal if the receiver does not have adequate dynamic range to capture strong and weak signals clearly.

This problem was observed in our tests, as we were receiving two signals: one 13.7 kilometers (DME) and the other 43.5 kilometers (ADS-B UAT) from our test site.

Calculating accurate ranging estimates from the two required determining a gain setting that had dynamic range adequate for receiving both signals clearly.

Vehicle Performance During experimental testing, the vehicle itself also underwent rigorous assessment of its performance under different conditions. Due to the delicate and often expensive nature of the payloads and experiments made possible by the JAGER platform, it is essential that the vehicle perform as expected, and that there are multiple procedures in place to protect the payloads in case of vehicle failure.

â–˛

FIGURE 6 Diagram showing the APNT experimental payload, and the proximity of the EMI-radiating CPU to numerous antennas.

Because the open-source autopilot had never been used with such a large vehicle, we first ground-tested the craft’s

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May 2015 | GPS World

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AUTONOMOUS FLIGHT | Security & Surveillance

▲

THE APNT PAYLOAD prior to installation of the DME antenna. The copper shielding on the CPU and antennas can be clearly seen.

flight control and stability. The vehicle was tethered and constrained to move in only one axis, and ropes were used to control its roll. While altering autopilot variables controlling roll and pitch feedback loops, we measured the vehicle’s response to impulsive disturbances and the time taken for it to right itself when upset. In this way we could tune the control gains and verify that the vehicle would be exceptionally stable during flight in even the most challenging atmospheric conditions. While we preferred to fly in the early morning hours to exploit clear air and lower winds, we did perform tests with momentary gusts of up to 7 m/s during envelope expansion flights.

We tested the vehicle with two accelerometers on board to measure how the rotors’ vibrations affected the rest of the craft.

One accelerometer was attached to the airframe itself, while the other was secured to the payload plate. A comparison of the acceleration data recorded by the two instruments revealed that the payload plate experienced significantly less vibration than the airframe during flight, and both measurements remained well within the tolerances advised by the airframe manufacturer.

Two crucial flight modes also were tested before payloads were flown on the vehicle. Both altitude-control mode and position-control mode were tested to ensure that they could precisely constrain respectively the vehicle’s altitude and absolute position in a range of atmospheric conditions.

Results showed that in altitude control mode, the vehicle’s z-coordinate was held constant to within ± 0.5 meters. In position control mode, its x- and y-coordinates remained within ± 1.0 meters (or a single vehicle length).

The success of the JAGER tracking mission also depends on accurate position measurements from the UAV. Operators must be confident in the vehicle’s position, so that ground forces can easily apprehend the located jammer, and also so that there is confidence in the success of safety protocols including geo-fencing, no-fly zones and minimum flight altitudes.

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GPS World | May 2015

In addition to the geo-fencing and flight precautions taken by the tracking algorithm, the JAGER UAV has several other safety procedures executed automatically by the autopilot.

A non-catastrophic error in the flight systems or payload is transmitted to the ground station for human troubleshooting, and commands can be sent to the vehicle as to how to proceed.

Finally, should we continue operations and allow its batteries to get sufficiently low, the vehicle will automatically return to launch site for landing and battery replacement.

A catastrophic failure such as the loss of a motor will result in an immediate controlled landing. The craft can also be commanded from the ground station to land or return to launch, and can be taken over by a human pilot at any time.

Other tests verified that the vehicle has the range and endurance to be successful when deployed in an airport setting. When fully loaded with APNT and tracking payloads, the UAV exhibited a top speed of 10 m/s, enough to cover the length of an A380-capable runway in less than 5 minutes.

A 20-minute flight endurance means that even including hovering during jamming signal observations by the tracking antenna, the JAGER system can hunt easily and effectively throughout an airport-sized area. Furthermore, we continue to explore techniques to improve dash capability, including reducing the weight of the APNT payload, and we anticipate describing results of these efforts in future reports.

Electromagnetic Interference Because of the payload tray’s small area (0.5 m2), electromagnetic interference (EMI) between APNT components was a significant issue during testing. The GPS and UAT receivers are extremely sensitive to interference from other sources emitting in the frequency ranges to which they are tuned. The APNT computer, by contrast, is composed of various processors, clocks, drives and power boards that emit powerful electromagnetic noise at a wide range of frequencies as a byproduct of their normal operation.

The size and mass of the APNT computer board meant that it had to be mounted in the center of the payload tray to avoid unbalancing the UAV. That left a maximum 7 centimeters of space around the computer on which to mount the two antennas (see FIGURE 6). With no shielding, the EMI from the computer proved powerful enough to completely overwhelm the GPS, UAT and DME network signals, making navigation and position estimation using any network impossible.

The EMI problem was solved in three ways. Masts were used to raise the receiving antennas to a height of 19 centimeters above the payload tray, the maximum height at which a mast collapse wouldn’t cause catastrophic rotor and vehicle failure.

The antennas also were moved around the edge of the payload tray so as to be furthest from the system components radiating at their particular frequency. Two devices that proved particularly problematic were the solid-state hard drive in the CPU and the telemetry radio antenna, which radiated EMI that www.gpsworld.com

Security & Surveillance |

interfered with the GPS and UAT frequencies respectively.

This was solved by moving the telemetry antenna to the underside of the craft, and the GPS antenna to the far side of the payload plate from the hard drive. The flexible design of the payload plate described earlier ensured that the relocation and testing of components was a straightforward process.

Shielding, however, proved to be the most important factor in eliminating EMI. Custom-made copper shields were added to the two masts to shield the antennas from the computer below them while still allowing an unobstructed view of the sky (see PHOTO). We tested numerous shielding iterations, including wire meshes and aluminum and lead foils; however; all were ineffective due to the strength and wide range of EMI wavelengths emitted. Finally, the computer itself was covered in a 2-millimeter layer of copper and 1-millimeter steel sheet. This combination struck the best balance between effectiveness and weight: aluminum was light but proved ineffective at shielding, while lead was very effective at EMI shielding but was too heavy for the UAV to carry.

Conclusions The development of the JAGER system contributes to U.S. preparation for a GPS jamming attack on civil aviation. While the first iteration described here is a significant improvement

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AUTONOMOUS FLIGHT

on previous jammer-hunting systems, future iterations of the JAGER UAV will be able to successfully navigate in a GPS-denied environment using alternative navigation signals including UAT and DME, and broadcast an accurate estimate of their position down to the ground.

The use of an octocopter flight system gives speed, maneuverability and sensory perception that far exceed any ground-based tracking effort. A fully loaded top speed of 10 m/s and almost instantaneous direction changes allow for efficient hunting over an airport-sized area and the location of a GPS jammer to within 30 meters, within a 20-minute flight endurance.

As the JAGER system can be entirely assembled from commercially available or open-source components and operates entirely autonomously, the system provides a lowcost, readily obtainable solution to the problem of GPS jamming. This means that it can be deployed quickly and is operable without extensive prior training.

The integration of autopilot, APNT navigation and tracking systems also allows for comprehensive monitoring and control of the UAV from the ground. Telemetry and data links to the ground station provide real-time updates as to the craft’s position, the jammer’s believed location and the status of all systems and instruments running on the

May 2015 | GPS World

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AUTONOMOUS FLIGHT | Security & Surveillance

vehicle. Safety protocols implemented in the software ensure that there is no risk of collision with site buildings, vehicles or personnel.

JAGER’s modular design gives operators extensive flexibility in situations that are capable of being successfully resolved by the system.

The switching of equipment and software to allow the UAV to use GPS navigation to hunt a UAT or DME jammer, for example, could be effected in a matter of seconds.

The JAGER system also provides a reliable test platform for any experiment that requires airborne operation. The exceptional stability of the airframe combined with extended flight time, high top speeds and pinpoint positioning lends the system to a wide variety of applications beyond jammer tracking, including network monitoring, atmospheric experiments and biological research.

Manufacturers The JAGER UAV airframe is a S1000 octocopter by DJI Innovations, Shenzhen, China; the flight batteries are a 8000 mAh model by Hextronik, Dongguan, China; the autopilot hardware and GPS antenna is a Pixhawk by 3D Robotics, Inc., San Diego, California; the autopilot software is based on PX4 by Pixhawk. org. The JAGER navigation GPS is made by u-blox, and the receiver for the APNT clock is made by Trimble.

The UAT hardware includes an ASR2300 multichannel transceiver by Loctronix Corporation, Woodinville, Washington; the tracking hardware comprises a 2.4 GHz Yagi antenna from L-com, North Andover, Massachusetts; an RN-XV Wi-Fi module by Roving Networks, Chandler, Arizona; and an Odroid-U3 computer by Hardkernel Co., Gyeonggi, South Korea.

JAMES SPICER is pursuing concurrent bachelor’s and master’s degrees in aeronautics and astronautics at Stanford University.

ADRIEN PERKINS is a Ph.D. candidate in aeronautics and astronautics at the Stanford University GPS Laboratory. He received his undergraduate degree in mechanical aerospace engineering at Rutgers University.

LOUIS DRESSEL is a graduate student at Stanford University. He received his undergraduate degree in aerospace engineering from Georgia Tech, with a minor in computer science.

MARK JAMES is a master’s student in aeronautics and astronautics at Stanford University.

YU-HSUAN CHEN is a research associate at the Stanford GPS Laboratory. He received his Ph.D. in electrical engineering from National Cheng Kung University, Taiwan.

SHERMAN LO is a senior research engineer at the Stanford GPS Laboratory.

DAVID S. DE LORENZO is a principal research engineer at Polaris Wireless and a consulting research associate to the Stanford GPS Laboratory.

PER ENGE is a professor of aeronautics and astronautics at Stanford University, where he is the Vance D. and Arlene C. Coffman Professor in the School of Engineering. He directs the Stanford GPS Laboratory.

June 22-25, 2015

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GPS World | May 2015

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High-Accuracy Laser Scans Aqua Drone for Offshore Missions The QuestUAV Aqua Pro is designed for offshore/onshore data-gathering in fields such as environmental, gas and oil, coast guard and security. It is a fixed-wing waterproof UAV based on the QuestUAV 200 airframe.

The Aqua Pro is capable of offshore missions and recovery in both freshand salt-water environments. It can withstand pressure differentials induced by rapid temperature changes, and overcome complexities of waterproofing/marine-grade electronics, sensors and avionics. It uses a GPS unit from SkyCircuits.

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The Riegl RiCopter is an unmanned multirotor UAS, integrating a highperformance and complete LiDAR system, the RIEGL VUX-SYS. The VUX-SYS comprises the VUX-1 LiDAR sensor, the Applanix AP20 IMU/GNSS system, a control unit, and up to four high-resolution cameras.

The Riegl RiCopter can acquire high-accuracy, high-resolution laser scan and image data.

The excellent measurement performance of the VUX-1 in combination with a precise fiberoptic gyroscope and GPS/GLONASS receiver results in survey-grade measurement accuracy in fields such as precision farming, forestry and mining. The IMU/GNSS unit provides roll and pitch accuracy of 0.015 degrees and heading accuracy of 0.035 degrees. Riegl is a maker of laser scanners, and using a high-end unmanned airborne platform allows data acquisition in dangerous and hard-to-reach areas.

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Survey-Grade Mapping Drone The eBee RTK by senseFly is a fully autonomous surveygrade mapping drone with a built-in L1/L2 GNSS receiver capable of receiving corrections from most leading brands of base station. This ensures high positional accuracy without the need for ground control points, so the aerial photography can produce orthomosaics and 3D models with accuracy down to 3 centimeters. It has 226 channels and tracks GPS L1, L2, L2C; GLONASS L1, L2, L2C; and SBAS. Sensefly, www.sensefly.com www.gpsworld.com

May 2015 | GPS World

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INNOVATION | Algorithms & Methods

Robustness to Faults for a UAV Integrated Navigation Systems Using Parallel Filtering Trevor Layh and Demoz Gebre-Egziabher THE NUMBER FOUR has special significance to humankind.

According to Penelope Merritt (a Samuel Beckett scholar) “[f]our has long been a number of completion, stability and predictability, as well as the representation of all earthly things.” And so it is with navigation systems. There are four important requirements of any navigation system: INNOVATION INSIGHTS accuracy, availability, continuity, and integrity. To quickly with Richard Langley review: Accuracy describes how well a measured value agrees with a reference value, typically the true value.

Availability refers to a navigation system’s ability to provide the required function and performance within the specified coverage area at the start of an intended operation.

Continuity is the ability of a navigation system to function without interruption during an intended period of operation.

Integrity refers to the trustworthiness of a navigation system. A system might be available at the start of an operation, and we might predict its continuity at an advertised accuracy during the operation. But what if something unexpectedly goes wrong? If some system anomaly results in unacceptable navigation accuracy, the system should detect this and declare that it can no longer be used for navigation at the expected accuracy level. GPS, for example, has built into it various checks and balances to ensure a fairly high level of integrity. The same may be said of other global navigation satellite systems. Satellite performance is continuously monitored and a satellite is set unhealthy when an anomaly is detected. Some receivers have built-in receiver autonomous integrity monitoring to detect and isolate problematic satellite signals and navigation support systems (such as the Wide Area Augmentation System) independently monitor the health of satellite signals and supply a timely warning in the case of anomalous signal behavior.

However, an aircraft, vessel, vehicle or some other platform still needs to be able to navigate if an independent primary navigation system becomes unavailable. This requires a backup system of some kind and may take the form of an inertial navigation system, another radionavigation system such as eLoran, celestial navigation or just dead reckoning. Ideally, the platform’s navigation system should have multiple integrated sensors so that it continues to operate seamlessly even in the event of a sensor failure. We would call such a system robust.

While we often use this word to describe a person with a strong healthy constitution, we can apply it to systems to refer to their ability to tolerate perturbations that might affect their functionality. A robust navigation system employs multiple sensors and uses appropriate filtering systems to autonomously detect anomalies, such as a failed sensor, and then to isolate it from the combined navigation solution.

It is important to catch navigation sensor failures early, ideally instantaneously, to reduce integrity risk as much as possible. This is not a trivial operation, and it requires clever software design and operation.

In this month’s column, we look at the development of such a robust navigation system employing a GNSS receiver, accelerometers, gyroscopes, magnetometers, an airspeed device and dead reckoning to supply a blended navigation solution to a flight control system on a small, unmanned aerial vehicle.

While the number four has special significance in religion, science and other aspects of our lives, the number five may be considered equally important — denoting, for example, how many digits we have on our hands and feet. For those mathematically inclined, it is the first safe prime number. And perhaps we should use it to more fully characterize a navigation system, denoting its accuracy, availability, continuity, integrity and robustness.

“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. To contact him, see the “Contributing Editors” section on page 6.

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GPS World | May 2015

M

ulti-sensor navigation systems generate an estimate of a vehicle’s state vector by fusing information from a disparate set of sensors. In many instances the sensors used in these systems provide redundant information. For example, in GNSS receivers, more than four (the minimum number required) satellite measurements are used to generate a position, navigation and time or PNT solution. This redundancy is bene¿cial because it enhances accuracy. It also enhances integrity or robustness because it allows the detection and possibly the isolation of failed sensors.

However, fault detection and isolation schemes do not work instantaneously because once a sensor has failed, it takes some time before this can be detected.

This is especially true for failures that are drift-like in nature as opposed to step-like. Drift-like errors grow slowly and, thus, fault detection schemes that monitor ¿lter residuals cannot detect them until they have grown to a point where they are sufficiently large to exceed preset thresholds.

The time between the onset of a fault and its detection — called the detection time — depends on the fault magnitude and thresholds of the fault detection algorithms. For a given fault magnitude, the length of the detection time represents a compromise between a navigation system’s continuity performance (or false alarm rate) and integrity risk (missed detection probability). The fact that faults cannot be detected instantaneously is an issue particularly for systems that have some form of dead reckoning (such as inertial navigation or velocity-based odometry) integrated with aiding sensors such as GNSS or radars. A failure in the aiding system (for example, a pseudorange fault in GPS) will lead to a corruption of the dead-reckoning solution. Once www.gpsworld.com

Algorithms & Methods |

the GNSS fault has been detected and subsequently removed, the error induced by this failure has already propagated into the dead-reckoning solution. How does one deal with these types of errors? In this article, we discuss a solution to this challenge, which we call parallel ¿ltering.

Solutions for dealing with the problem exist. For example, one approach that has been used is based on the idea of delayed measurements. In this approach, integration of aiding sensor measurements in the navigation solution is delayed until a period equal to the fault detection time has elapsed.

If no faults are detected during this period, then the delayed measurements are extrapolated forward in time and integrated into the navigation solution. Alternately, we can rewind the dead-reckoning solution backwards in time, integrate the delayed measurements and fast-forward the integrated solution up to the current time epoch. While this approach works, it has several shortcomings, of which we will mention just two. First, it requires buffering sensor data. Second, the most current navigation solution is not as accurate as it can be, because it does not incorporate the most recent sensor measurements (that is, the delayed measurements). The parallel ¿ltering approach and fault tolerance we describe in this article deals with both of these shortcomings. Of course, like any other engineering solution, it represents a compromise between competing requirements. We will discuss these compromises and their impacts later in the article. For now, we will concentrate on describing the mechanics of parallel ¿ltering and its performance when implemented in an integrated Àight control system used for navigation, guidance and control of small unmanned aerial vehicles or UAVs.

Parallel Filtering To understand parallel ¿ltering, consider the schematic in FIGURE 1, which represents the conventional way in which an integrated navigation system fuses the information from N sensors. All the measurements from the N sensors are integrated in a single sensor-fusion algorithm. In the context of what we are describing here, the algorithm consists of a navigation ¿lter and a fault-detection ¿lter. The sensor-fusion algorithm integrates the measurements from all N sensors and generates a single, optimal estimate of the navigation state vector.

In contrast to this, the schematic shown in FIGURE 2 is the parallel ¿ltering approach introduced in this article. In this case, the same N sensors are divided up into M separate sensor clusters. The measurements from the sensors in the jth cluster is processed in a sensor-fusion algorithm to generate an estimate of the state vector denoted xj and a covariance matrix Pj. Each pair (xj, Pj) is then sent to a blending ¿lter that generates a single optimal estimate and P. The estimate is a weighted sum of the estimates from the M ¿lters:

(1) www.gpsworld.com

INNOVATION

y1

y2

y3

Navigation and fault detection filter

x

y4

yN ▲

FIGURE 1 Conventional (centralized) sensor fusion architecture.

where Bj are blending weights that function as switches, which can be “opened” (set to zero) to isolate a parallel ¿lter momentarily or permanently when a failed sensor is detected.

The analogy with a physical switch should not be taken literally, however, because they are not “hard on-off” switches.

Instead, they are matrices, which serve to change the emphasis put on a particular parallel ¿lter. The blending weights are calculated so that the estimate is an unbiased minimumvariance estimate. In mathematical terms, this means that they minimize the trace of the ¿nal covariance P. We will give more detail on how to calculate the weights shortly, but before we do that, let us describe, at a high level, how all of this works.

Consider that one of the sensors in the th cluster fails. The th fault detection ¿lter will identify the fault and try to isolate it.

If the fault is non-isolable, the th fault detection ¿lter will raise an alarm. This can be done in various ways including inÀation of the th ¿lter covariance . An increasing covariance matrix leads to a decreasing value of the corresponding blending weight . For a non-isolable fault, will eventually approach zero, which effectively isolates the th cluster from the navigation solution. If the fault was just a momentary glitch, then and are reset. In the simplest case, can be reset to a weighted sum of remaining M-1 parallel state estimates.

This is then blended with all of the other parallel estimates for generating the new . This does not require setting aside buffers to store delayed measurements. Neither does it require rewinding the solution back in time when recovering from a faulted sensor scenario.

Mathematical Formulation Providing a detailed derivation of the parallel ¿lter is beyond the scope of this short article. Instead, we will just summarize the steps in the parallel ¿ltering algorithm with the key May 2015 | GPS World

41

INNOVATION | Algorithms & Methods

y1

Navigation and fault detection filter #1

B1

(9)

y2

where

Navigation and fault detection filter #2

B2

y3

Blending filter

x

yN-1 Navigation and fault detection filter #M

BM

yN ▲

FIGURE 2 Parallel filtering architecture.

formulas that are used in determining the blending weights.

For simplicity, we will assume that we are working with a system with two parallel ¿lters (M = 2 in Figure 2). How this extends to systems with more parallel ¿lters or complex interlinking between the ¿lters will become apparent later in the article when we present the results from a case study.

To start, let us de¿ne some notation. We assume that the two parallel ¿lters are extended Kalman ¿lters (EKFs) generating estimates of the state vectors x1 and x2. We will denote these estimates and . The covariances for these estimates are denoted by P1 and P2, respectively. The output of the blending ¿lter is an estimate of the state vector x, which is a subset of x1 and x2. In mathematical terms, this means that we can de¿ne two mapping matrices M1 and M2 whose entries are either “1” or “0” and: and

.

and Π is given by:

(2)

(10) where P12 is the cross-correlation between the states of parallel ¿lter #1 and #2. We will say more about this shortly.

In the meantime, note that in Equation (9), P1 and P2 are the covariances computed by the parallel ¿lters after the measurement update. This computation requires knowledge of K1 and K2, which are the EKF gains for parallel ¿lters. The matrices H1 and H2 are the observation matrices for ¿lters #1 and #2. They relate the measurements y1 and y2 of the two parallel ¿lters to their respective state vectors as follows (refer to Figure 2): (11) (12) where v1 and v2 are the measurement noises. Thus, the blending ¿lter has to have knowledge of the measurement model and the gains of each parallel ¿lter.

Finally, note that P12 is zero if the dynamic models (time update equations) for the two parallel filters are completely independent. However, if they share sensors then there will be a correlation and P12 ≠ 0. This is the case for the example we present later in this article. In this case, P12 needs to be propagated between measurement updates. This can be done with the covariance time update equation (Lyapunov equation) for the joint state vector

The output of the blending ¿lter is, thus, given by: .

(3)

The blending weights are computed from: (4) (5) where (6) (7) .

(8)

The covariance of is given by: ▲

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GPS World | May 2015

FIGURE 3 Goldy flight control system. www.gpsworld.com

Algorithms & Methods |

INNOVATION

.

Note that the architecture depicted in Figure 2 is meant to be a high-level depiction of the idea of parallel ¿ltering. It should not be interpreted as an actual system architecture schematic.

This will become apparent in the case study we present later in this article. The system we will consider there consists of three ¿lters of which two are run in series (cascaded so that the output of the ¿rst is the input of the second) and each, in turn, is run in parallel with the third ¿lter.

It is important to note that the proper blending of the various ¿lters’ outputs hinges on an accurate estimate of the individual covariances. This is particularly true when a fault has occurred.

An individual ¿lter that has detected a failed sensor must inÀate its covariance to reÀect its faulted state. How a ¿lter does this is the problem of fault-detection ¿lter design and is beyond the scope of this short article. For the work presented here, we used fault-detection ¿lters, which monitored the EKF measurement residuals to detect sensor faults. When these ¿lters detected a fault, they immediately inÀated the faulted sensor’s output noise covariance matrix. We cannot overemphasize, therefore, the importance of having a well-designed fault-detection ¿lter that responds in a timely and accurate manner to sensor faults.

B1b

Blending filter #1

Blending filter #2

B2

Airspeed DR filter

B3

Pitot tube ▲

FIGURE 4 Goldy parallel filtering architecture. The three-axis magnetometer (Mag.) feeding the attitude heading reference system (AHRS) filter is part of the inertial measurement unit (IMU) device. The device’s accelerometer and gyro outputs feed both the GNSS-INS and AHRS filters. A pitot tube device supplies airspeed measurements to the airspeed-based dead-reckoning (DR) filter.

Case Study: Small UAV Flight Control As a case study for the parallel ¿ltering approach and fault detection/isolation scheme described above, we discuss the results of a blending ¿lter, which was used on the University of Minnesota UAV Laboratory Goldy Àight control system

K528

K508 GPS L1/L2/L5 GLONASS L1/L2 BeiDou B1/B2/B3(support)

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K500

K501G GPS L1/L2 GLONASS L1/L2

K501

GPS L1/L2 BeiDou B1/B2

GPS L1 GLONASS L1 BeiDou B1

GPS L1/L2 BeiDou B1/B2 /B3(support)

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May 2015 | GPS World

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GPS unavailable

250

300

350

400

200

250

300

350

400

10 5 0 −5 −10 −15 350

FIGURE 5 Attitude errors. The gray vertical lines indicate when GPS availability was interrupted and then restored.

(FCS) shown in FIGURE 3. The Goldy FCS is used for navigation, guidance and control of small UAVs. The results presented below were obtained by post-processing flight test data.

The architecture of the parallel filtering scheme used is shown in FIGURE 4. There are three separate filters whose outputs are blended: a GNSS-aided inertial navigation system (INS) filter, an attitude heading reference system (AHRS) filter and an airspeed-based dead-reckoning (DR) filter. Two blending filters are used to fuse the outputs from these three filters. The first blending filter fuses the attitude estimates from a GNSSaided INS and an AHRS. The second blending filter fuses the position solutions from the GNSS-aided INS and the airspeedbased DR system. The AHRS and the airspeed-based DR filters are a pair of filters, which are cascaded to generate an estimate of the UAV navigation state vector. Thus, in the case of GNSS-denied operations, it can provide a position, velocity and attitude estimate to the flight control system. All of the sensors and software required to run these filters are part of the Goldy FCS. Before we present results of the parallel filter’s performance, we will briefly describe these three systems below.

The GNSS-aided INS uses a consumer/automotive grade inertial measurement unit (IMU) to generate a position, velocity and attitude solution at a rate of 50 Hz. A 1-Hz measurement update from GPS is used to arrest drift errors inherent in inertial navigation systems, especially those mechanized using low cost consumer/automotive grade sensors. The GPS position updates also allow estimation of the inertial sensor biases. The state vector for this GNSS-aided INS is denoted x1 and consists of the following 15 states: latitude (Λ), longitude (λ), altitude (h), north velocity (Vn), east velocity (Ve), down velocity (Vd), roll angle (φ), pitch angle (θ), yaw angle (ψ), three gyro biases (bp, bq and br) and three accelerometer biases (bax, bay and baz).

The second and third filters are a pair of estimators connected in series. The AHRS filter generates attitude estimates, which are fed to the airspeed-based DR filter. The AHRS uses the same IMU as the GNSS-aided INS to estimate roll (φ), pitch (θ) and yaw (ψ) attitude states of the vehicle as well as the three gyro biases (bp, bq and br). This AHRS filter’s six-dimensional state vector is denoted x2. The attitude is then used to resolve airspeed measurements from the body frame of the UAV to the north-east-down coordinate frame. After adding an estimate 44

GPS World | May 2015

15000

10000

5000

400

0

▲

200

250

300 Time (seconds)

350

400

FIGURE 6 Position errors during a GPS outage.

GPS unavailable

Yaw weight

300 Time (seconds)

Pitch weight

250

Position error (meters)

200

200

▲

GNSS-aided INS AHRS Blending filter

GNSS-aided INS AHRS Blending filter

Roll weight

Yaw error (degrees)

10 5 0 −5 −10

Roll error (degrees)

10 0 −10 −20

Pitch error (degrees)

INNOVATION | Algorithms & Methods

▲

1.0 0.8 0.6 0.4 0.2 0.0 0 1.0 0.8 0.6 0.4 0.2 0.0 0 1.0 0.8 0.6 0.4 0.2 0.0 0

AHRS GNSS-aided INS

100

200

300

400

500

100

200

300

400

500

100

200 300 Time (seconds)

400

500

FIGURE 7 Attitude blending weights.

of the local winds to this, a single integration yields a position solution. This is done at a rate of 50 Hz. A periodic 1-Hz update from GPS is used to arrest the inherent DR drift. It also allows estimation of the magnitude of the local winds. The state vector of the airspeed-DR is denoted x3 and consists of the following 11 states: latitude (Λ), longitude (λ), altitude (h), local north wind speed (Wn), local east wind speed (We), yaw angle offset (Δψ), pitch angle offset (Δθ), three airspeed-measurement biases (Ub, Vb and Wb), and altitude offset (Δh).

In the UAV flight control system, the blended states of interest are position (Λ, λ and h) and attitude (φ, θ and ψ). This implies that four mapping matrices are required for the fusion.

First, matrices are needed for the attitude blending using the GNSS-aided INS (M1a) and the AHRS (M2). Then, additional matrices are needed for the position blending using the GNSSaided INS (M1b) and the airspeed-based DR (M3). The shaping matrices are given by: (13)

(14)

(15)

(16) www.gpsworld.com

Algorithms & Methods |

INNOVATION

where Ij×k is a j × k identity matrix and Zj×k is a j × k matrix of zeros.

▲

FIGURE 8 Position blending weights.

FIGURE 5 shows the errors in the attitude of all three ¿lters during this Àight test. It shows that the blended estimates of heading, pitch and roll tend to oscillate closer to zero error than either of the individual ¿lters themselves. This is reÀected in TABLE 1, where it can be noted that the root-mean-square (RMS) error of the blended solution is lower than either the GNSSaided INS or the AHRS in each of the three attitude solutions.

FIGURE 6 shows the position errors of all three systems and illustrates one of the primary advantages of the proposed architecture. FIGURE 7 and FIGURE 8 show the blending weights matrices B1 and B2 before, during, and after the GPS outage.

Image L'Avion Jaune 2014

Filter Performance Validation of the parallel ¿ltering scheme was accomplished by post-processing data from a series of Àight tests where the Goldy FCS was installed on a UAV Àying around a boxshaped trajectory.

The ¿rst set of results was from a case where GPS was available from the moment the FCS is turned on until shortly after takeoff. Thus, GPS was available during initialization, take off roll and initial climb of the UAV. Then, GPS services were interrupted for a three-minute period during Àight and restored shortly before the UAV landed. The GPS interruption was simulated by cutting out the 1-Hz measurement updates to the GNSS-aided INS and the AHRS/airspeed-DR system. In the background, however, there was another GNSS-aided INS that had an uninterrupted GPS service throughout the entire Àight. This additional GNSS-aided INS solution is referred to as the reference solution and is used as ground-truth for assessing the performance of the parallel ¿ltering scheme. For example, error plots shown below were generated by taking the difference between the various ¿ltering schemes under consideration and this reference solution.

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INNOVATION | Algorithms & Methods

▲

FIGURE 9 GPS sensor errors during a fault.

What is shown in these ¿gures are the diagonal elements of these matrices.

The INS exhibits extreme drift errors after only three minutes of unaided operation. The blending algorithm detects this inaccuracy and places more weight on the slow-drifting AHRS-DR solution, as shown in Figure 8. When GPS services are restored, the GNSS-aided INS error is “reset,” and the position weights are re-established to their pre-outage levels with minimal transient responses.

We next show data from another Àight test where an unplanned but fortuitous fault in the GPS sensor occurred.

The cause of this fault has not been de¿nitively determined,

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but potential reasons for it include loose cabling or outdated ¿rmware. Nevertheless, this fault provided useful Àight data for our architecture as no ¿ctitious or simulated data was used.

FIGURE 9 shows the GPS altitude measurements during this Àight test. At t = 44 seconds a large oscillatory GPS error occurred.

Similar errors were present in the GPS measurements of the velocities, latitude and longitude.

Thus, all ¿lters were initialized and operated correctly for the ¿rst 44 seconds. Between 44 and 132 seconds, the GPS receiver output was in error. This time period corresponds to the taxi, takeoff and initial climb phase of the UAV’s Àight.

A “reference” GNSS-aided INS, which did not employ the fault detection and isolation scheme that was employed in the parallel ¿ltering system, was running in real time for this Àight test. However, the UAV was under manual control (fortunately). As shown by the gray solution in FIGURE 10, the “reference” (non-fault-tolerant) system running in the background diverged and never converged.

The dark traces in Figure 10 show the performance of the fault detection and isolation algorithm paired with the parallel ¿ltering scheme described in this article. It is seen to be faulttolerant and ignores the invalid measurements. Although nearly no aiding was provided until after the GPS sensor converged back to a stable state, the fault tolerant ¿lter provided a much more accurate solution.

A bird’s eye view of the ground track of the UAV shows a similar trend. This can be seen in the position plot of FIGURE 11, which shows a roughly 60-second segment of the Àight.

This north vs. east plot demonstrates that a non-fault-tolerant GNSS-aided INS provides an unstable position solution similar to the attitude shown in Figure 10. By contrast, the fault-tolerant system described in this article provides a smooth position estimate that ignores the “bad” GPS measurements and tracks the “good” measurements after they convergence back to the truth. Therefore, the safety of the aircraft would not have been in question, and the UAV could have completed multiple segments of fully autonomous waypoint navigation in spite of the faulty sensor measurements provided earlier.

Summary The parallel ¿ltering approach discussed in this article has the potential for providing a systematic way of designing

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GPS World | May 2015

FIGURE 10 Attitude solution during an actual GPS sensor failure. www.gpsworld.com

Algorithms & Methods |

multi-sensor navigation systems, which are robust to sensor faults. Unlike prior approaches, it obviates the need to maintain data buffers to store data, which can be played back in the event of a sensor fault. As noted earlier, like any engineering solution to problems, this one is a comprise between many competing requirements. As such, it has some drawbacks when compared to traditional approaches.

We note two of them here as they are the focus of ongoing work. First, the computational overhead associated with this approach can be high especially if a large number of parallel ¿lters are used. Thus, methods for streamlining the computations so that they are not computer-resource intensive will be important.

The second issue that needs further exploration is the way in which blending weights are computed. A key input to calculating the weights (as well as the “triggers” for the fault detection and isolation algorithm) are the covariances estimated by the various parallel filters. This can be problematic if the covariances used by the parallel ¿lters do not match the true statistics. This can lead to turning off a particular ¿lter when no faults had occurred or, worse, retaining a ¿lter with a failed sensor in the blended solution.

For more detail about the Goldy FCS, go to www.uav.aem. umn.edu.

INNOVATION

Solution

Heading RMS error

Pitch RMS error

Roll RMS error

GNSS-aided INS

13.38

5.73

5.84

AHRS

7.16

2.93

4.49

Blending filter

6.61

2.85

4.45

▲

TABLE 1 RMS orientation errors of different solutions (in degrees).

Manufacturers The Goldy FCS uses a Hemisphere GNSS ( www. hemispheregnss.com) Crescent OEM board and an Analog Devices, Inc. (www.analog.com) ADIS16405 iSensor MEMS inertial measurement unit.

TREVOR LAYH is a M.S. candidate in the Department of Aerospace Engineering and Mechanics at the University of Minnesota in Minneapolis.

He obtained his B.S. in mechanical engineering from South Dakota State University, Brookings, S.D., and his research interests include backup navigation systems to GPS-aided inertial navigation systems.

DEMOZ GEBRE-EGZIABHER is an associate professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota. His research focuses on the design of multi-sensor navigation systems. He holds a Ph.D. in aeronautics and astronautics from Stanford University, Stanford, Calif.

Acknowledgments This article is based, in part, on the paper “A Fault-Tolerant, Integrated Navigation System Architecture for UAVs” presented at ION ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif., January 26–28, 2015. The contents of this article reÀect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The authors acknowledge the United States Department of Homeland Security for supporting the work reported here through the National Center for Border Security and Immigration under grant number 2008-ST-061-BS0002. However, any opinions, ¿ndings, conclusions or recommendations in this article are those of the authors and do not necessarily reÀect views of the United States Department of Homeland Security.

▲

FIGURE 11 GPS sensor failure performance: north vs. east.

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INNOVATION | Algorithms & Methods FURTHER READING ◾ Authors’ Conference Paper “A Fault-Tolerant, Integrated Navigation System Architecture for UAVs” by T. Layh and D. GebreEgziabher in Proceedings of ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif. January 26–28, 2015, pp. 702–712.

◾ Attitude Heading Reference System and Airspeed-Based Dead Reckoning Filters Correlated-Data Fusion and Cooperative Aiding in GNSS-Stressed or Denied Environments by H. Mokhtarzadeh, Ph.D. dissertation, University of Minnesota UAV Laboratories, 2014.

“A Recovery System for SUAV Operations in GPS-Denied Environments Using Timing Advance Measurements” by T. Layh, J. Larson, J. Jackson, B. Taylor and D. GebreEgziabher in Proceedings of ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, Calif. January 26–28, 2015, pp. 293–303.

◾ UMN UAV Research Lab and Goldy Flight Control System “Infrastructure” website, University of Minnesota UAV Laboratories, http://www.uav.aem.umn.edu/wiki/ Infrastructure, July 2014.

◾ Navigation in GPS-Denied Environments Impact and Mitigation of GPSUnavailability on Small UAV Navigation, Guidance and Control

by D. Gebre-Egziabher and B. Taylor, Technical Report 2012-2, University of Minnesota, Department of Aerospace Engineering and Mechanics, November 2012. Available through online request: https://www.aem.umn. edu/cgi-bin/bib/bib-request?QFCHK= 0&entry=1170&return=%2Fpeople%2 Ffaculty%2Fbio%2Fgebre%2Fpublicat ions%2Eshtml

Published by American Institute of Aeronautics and Astronautics, Reston, Va., 2003.

◾ Example of a Fault-Tolerant Avionics System “Performance of Honeywell’s Inertial/ GPS Hybrid (HIGH) for RNP Operations”

by C. Call, M. Ibis, J. McDonald and K. Vanderwerf in Proceedings of PLANS 2006, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Coronado (San Diego), Calif., April 25–27, 2006, pp. 244–255, doi: 10.1109/ PLANS.2006.1650610.

◾ GNSS Integrity

Introduction to Avionics Systems, 2nd Edition, by R.P.G Collinson. Published

by Kluwer Academic Publishers, Boston, Mass., 2003.

“Digging into GPS Integrity: Charting the Evolution of Signal-in-Space Performance by Data Mining 400,000,000 Navigation Messages”

by L. Heng, G.X. Gao, T. Walter and P. Enge in GPS World, Vol. 22, No. 11, November 2011, pp. 44–49. Available on line: http://gpsworld.com/gnsssysteminnovation-digging-gpsintegrity-12254/

Civil Avionics Systems by I. Moir and A. Seabridge. AIAA Education Series.

“Integrity for Non-Aviation Users: Moving Away from Specific Risk” by

◾ Avionics Reliability

S. Pullen, T. Walter and P. Enge in GPS World, Vol. 22, No. 7, July 2011, pp. 28– 36. Available on line: http://gpsworld. com/transportationroadintegrity-nonaviation-users-11847/ “The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63. Available on line: http://www2.unb.ca/gge/Resources/ gpsworld.march99.pdf

◾ Multi-Sensor Systems “Toward a Unified PNT — Part 1: Complexity and Context: Key Challenges of Multisensor Positioning”

by P. D. Groves, L. Wang, D. Walter, H. Martin and K. Voutsis in GPS World, Vol. 25, No. 10, October 2014, pp. 18, 27–34, 47–49. Available on line: http:// gpsworld.com/toward-a-unified-pntpart-1/ “Toward a Unified PNT — Part 2: Ambiguity and Environmental Data: Two Further Key Challenges of Multisensor Positioning” by P. D. Groves, L. Wang, D. Walter and Z. Jiang in GPS World, Vol. 25, No. 11, November 2014, pp. 18, 27–35. Available on line: http://gpsworld.com/toward-aunified-pnt-part-2/

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ON THE EDGE

Protecting Position in Critical Operations Jamming Signals Criminal Activity in Intermodal Ports Logan Scott

M

ore than 25 million containers pass through U.S. intermodal ports every year, with port operations valued at more than $1 billion per day. Measured in 20-foot equivalent units (TEU), the World Bank reports that worldwide, more than 600 million TEU passed through intermodal ports in 2012: 155 million through Chinese ports, 95 million through the EU ports and 43 million through U.S. ports.

The Port of Long Beach alone handled 6,820,806 TEU in 2014. GPS is a central component of automated port operations, but because GPS is widely used in asset tracking and monitoring, it has also become a target for denial-of-service attacks. If we look to the history of computer security, the initial attacks were mostly nuisances, but as criminals ¿gured out how to monetize attacks, the attacks became more damaging, more sophisticated and more pro¿table.

In January, the U.S. Coast Guard held a public meeting on Maritime Cybersecurity Standards at Department of Transportation headquarters in Washington, D.C. Brett Rouzer, chief of Maritime Critical Infrastructure and Key Resources Protection, Coast Guard Cyber Command, described how a major East Coast intermodal shipping facility was degraded by a GPS disruption for more than seven hours. Two ship-to-shore cranes ceased operation due to loss of position, and two others were degraded. Ports are highly automated; ship-to-shore cranes are just one of the container-handling systems critically reliant on GPS. Fully automated ports providing services for unmanned container ships, trucks and trains lie within the realm of feasibility in the near future.

Rouzer did not specify the motivates for the disruption, www.gpsworld.com

how the attack was mounted, or if the shipping facility was even the intended target of the attack (I suspect it was not).

Jamming is not a highly selective process, and it can affect numerous unintended targets.

In June 2014, I reported to the PNT Advisory Board on how every third or fourth truck on Highway 30B near Portland (Oregon) International Airport was radiating at or near the GPS L1 frequency. This highway leads to several nearby Port of Portland intermodal terminals west of the airport. The Federal Bureau of Investigation recently reported that “In 46 reported incidents, the thieves placed one or more GPS jammers in cargo containers with stolen automobiles” (italics mine). High-end automobiles command premium prices in foreign markets and are stolen and shipped out of the country within hours, usually via intermodal container.

Active jammers can affect not only the automobile’s GPS tracker, but also trackers on other containers, ship’s navigation systems, straddle carriers and ship-to-shore cranes. Again, jamming is not selective.

Of particular note as cited above, criminals are beginning to use multiple jammers. Car theft rings are not unique in this.

According to the Pharmaceutical Cargo Security Coalition in July 2014, “a tractor and trailer hauling $2 million worth of pharmaceutical products was stolen from a truck stop in Cartersville, Georgia, with the thieves deploying two separate GSM jammers.” The criminal’s motivation is that tracking devices can be hard to find and disable; just because you found one doesn’t mean that there isn’t another. The use of multiple jammers in criminal enterprise is indicative of a threat escalation where bad actors are seeking higher effect.

This could lead to higher jamming powers and so on; and also more collateral damage.

May 2015 | GPS World

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Response What is a correct and measured response to threats against navigation and timing? The key is to be on the lookout for emerging threats and to have a Àexible response. Early detection usually yields a more effective and lower cost response; witness Ebola and ISIS. Following a public health model would seem to offer better prospects for protecting access to PNT. To this end, I would argue that situational awareness is the ¿rst important step.

One of the most striking comments that Sarah Mahmood (DHS) made at last June’s PNT Advisory Board meeting was about how backup systems are often not activated or used because the GPS receiver fails to recognize that there is a problem. As we move towards resilient PNT architectures, one of the most critical needs is to be able to distinguish good signals from bad signals and act accordingly.

Most GNSS receivers already have fairly advanced jamming detection capabilities by virtue of having an automatic gain control. Sudden changes in precorrelation input power levels are not normal and can indicate jamming or RF spoofing. Many GNSS receivers, particularly those that go into embedded mobile applications, also have sophisticated spectrum- and temporal-analysis capabilities, used mainly for diagnostic purposes in looking for interference sources from other components of the device. This same capability can be used in detecting and fingerprinting jammers. We already have the smoke alarms; we must amplify their use and visibility to the wider community of GNSS users and beyond.

Detection One notable aspect of the port incident was the duration: more than seven hours. Rapidly ¿nding and disabling the jammer was clearly a problem in this case. The old adage is that to ¿nd a stationary source (jammer) you need to be moving, and to ¿nd a moving source, you need to be stationary. Trucks and trains entering or leaving a port all pass through gates that can act as a simple chokepoint for detecting and ¿nding active jammers. Properly hardened ship-to-shore cranes and straddle carriers can also act as a chokepoint. Straddle carriers used in moving containers around the yard and between modes could be very good at ¿nding stationary jammers.

There are numerous relatively low-cost approaches for finding jammers in support of enforcement actions. One additional point: law enforcement officials need to be better educated as to why they should be interested in jammers; jammers point towards a crime much like smoke points to a fire.

Given the economic criticality of port operations and

Photo credit: Hannes Grobe (Wikimedia Commons, CC-BY 3.0)

ON THE EDGE

▲

INTERMODAL FREIGHT TRANSPORT uses standardized cargo containers to transport freight using multiple transportation modes (ship, rail and truck) without having to handle the freight itself when changing modes.

the concentration of assets (and asset trackers), we may see increased incidence of GPS disruptions. The situation is not critical yet, but it does bear watching. If jamming events increase or it takes too long to find and disable jammers, improved operational resilience will be needed.

Inertial measurement units are already used in many critical applications, but they don’t offer long-duration capability.

They drift. Using adaptive arrays in critical equipments is another possibility, but they are not a panacea. Adaptive arrays are physically large, and standard null-steering approaches are not compatible with RTK processing. Precise positioning systems based on GNSS require specialized antenna-receiver designs to achieve a high level of jam resistance.

While I strongly believe eLoran is an urgently needed augmentation for resilient wide area navigation, it is not capable of the centimeter-level precision required for machine control, for example ship-to-shore cranes and straddle carriers.

High-precision local-area positioning systems based on optical systems, RFID and/or Locata-style systems may be the best approach for creating a defense in depth.

And then there is the cybersecurity question, which I will leave for another day.

LOGAN SCOTT has 35 years of military and civil GPS systems engineering experience. He is a consultant specializing in radio frequency signal processing and waveform design. At Texas Instruments, he pioneered approaches for building high-performance, jamming-resistant digital receivers. He is a co-founder of Lonestar Aerospace, an advanced decision analytics company in Texas. Logan is a Fellow of the Institute of Navigation and holds 37 U.S. patents.

Note: A video of the Coast Guard meeting is available at: https://www.youtube.com/embed/rzOVc1ZOuvY?rel=0. Rouzer’s talk starts at 36:30, with the port jamming incident mentioned at 48:51.

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