Instructors
November 2 - 6, 2015 Westin Tysons Corner Hotel 7801 Leesburg Pike Falls Church, Virginia 22043 USA
Course 556 (2.7 CEUs)
Inertial Systems, Kalman Filtering and GPS/INS Integration DAY 1
DAY 2
DAY 3
DAY 4
Dr. Alan Pue, Johns Hopkins University, Applied Physics Lab. 8:30 Introduction to INS/GPS integration ●● Inertial navigation ●● Integration architectures ●● Example applications Vectors, Matrices, and State Space ●● Vectors and matrices ●● State-space description ●● Examples Random Processes ●● Random variables ●● Covariance matrices ●● Random process descriptions
Inertial Navigation Mechanization ●● Gravity model ●● Navigation equations ●● Implementation options Inertial Sensor Technologies ●● Accelerometer technologies ●● Optical gyros ●● MEMS technologies ●● Technology survey Strapdown Systems ●● Quaternions ●● Orientation vector ●● Coning and sculling compensation
Mr. Michael Vaujin
Dr. Alan Pue
DAY 5
Mr. Michael Vaujin, Aerospace, Navigation & Defense Consultant
Day 3 Morning INS Aiding of Receiver Tracking ●● Code and carrier tracking ●● Track loop design trades ●● Interference suppression ●● Deep integration Tightly-Coupled INS/GPS Design ●● Measurement processing ●● Filter parameter selection ●● Pseudo-range and delta pseudorange measurement models Multi-Sensor Integration ●● Terrain aiding and relative GPS ●● Carrier phase differential integration ●● GPS interferometer/INS integration
Aided Phi-angle Navigator Demonstration ●● Description and demonstration of a tightly GPS aided 17-state phiangle navigator under and aircraft style trajectory ●● Solution to homework problems ●● The need for integrated velocity error states and their application to Doppler aiding vs. using delayed states Methods of Smoothing ●● Optimal prediction, fixed interval smoothing and demo ●● Fixed point smoothing and fixed lag smoothing ●● Application to navigation ●● Computer demo Square Root Filtering ●● The need for square root filtering ●● Square root covariance filtering and smoothing ●● Information and square root information filtering ●● UD filtering ●● Computer demo
Day 1 of Review is Optional
Initialization, Ground Alignment and Transfer Alignment ●● Coarse leveling ●● Large heading error equations ●● Gyro-compassing ●● Transfer alignment and boresight error states ●● ECEF navigation ●● Gravity error as consider states ●● Computer demo Particle Filtering and miscellaneous topics ●● Particle filters and their use in navigation ●● Trajectory generation ●● Pseudo-measurements ●● Numerical Jacobian computation ●● Computer demo Radar Tracking of a ballistic re-entry body ●● Examining the limitations of EKFs using an example with a highly non-linear plant and measurements ●● Results using a UKF and a particle filter ●● Computer demo
Lunch is on your own 12:00-1:30 PM
5:00
Kalman Filter ●● Filtering principles ●● Least squares estimation ●● Kalman filter derivation
Navigation System Errors ●● Tilt angle definitions ●● Navigation error dynamics ●● Simplified error characteristics
Filter Implementation ●● Filter processing example ●● Off-line analysis ●● Filter tuning
System Initialization ●● INS static alignment ●● Transfer alignment ●● Simplified error analysis
Navigation Coordinate Systems ●● Earth model ●● Navigation coordinates ●● Earth relative kinematics
Loosely-Coupled INS/GPS Design ●● Measurement processing ●● Filter design and tuning ●● Navigation system update
Day 1 of Review is Optional
Building Extended Kalman Filters ●● Radar tracking of a body with non-linear dynamics ●● Radar tracking of an accelerating body with non-linear measurements ●● Computer demo
Adaptive Filtering ●● Online and offline residual analysis ●● Advance methods of outlier detection ●● Multiple model adaptive estimation ●● Application to navigation ●● Tracking, & fault detection ●● Computer demo
Kalman Filter Methods of Implementation ●● Covariance matrix maintenance ●● Sequential and batch measurement processing ●● Measurement de-correlation ●● Matrix partitioning
Unscented Kalman Filters ●● Sigma points and unscented transforms ●● Augmentation for non-additive noise ●● Application to navigation ●● Computer demo
Aided Psi-angle Navigator Demonstration ●● Description and demonstration of a loosely GPS aided 15-state psiangle navigator under an aircraft style trajectory ●● Linearization homework problems
Optimal and Sub-Optimal Covariance Analysis ●● Effects of mis-modeling ●● Optimal and sub-optimal (2 pass) covariance analysis ●● Error budget analysis ●● Reduced state analysis ●● Computer demo
Mr. Michael Vaujin, Aerospace, Navigation & Defense Consultant
Course Objectives
Course 556 is an expanded version of the former Course 546. It was expanded based on student requests for more review material in the first day and for even more content depth and computer MATLAB® demonstrations. This course on GNSS-aided navigation will thoroughly immerse the student in the fundamental concepts and practical implementations of the various types of Kalman filters that optimally fuse GPS receiver measurements with a strapdown inertial navigation solution. The course includes the fundamentals of inertial navigation, inertial instrument technologies, technology surveys and trends, integration architectures, practical Kalman filter design techniques, case studies, and illustrative demonstrations using MATLAB®.
Who Should Attend?
GPS/GNSS professionals who are engineers, scientists, systems analysts, program specialists and others concerned with the integration of inertial sensors and systems. Those needing a working knowledge of Kalman filtering, or those who work in the fields of either navigation or target tracking.
Prerequisites
Familiarity with principles of engineering analysis, including matrix algebra and linear systems.
You Asked: We Answered! Based on attendee requests, our popular Course 546 has been expanded to Course 556 (to 4.5 days) to allow sufficient time for a day of high-level review leading to the enhanced course content. The Day 1 review may be skipped by those with sufficient mastery of the content and high-level mathematics associated with these topics.
A basic understanding of probability, random variables, and stochastic processes. An understanding of GPS operational principles in Course 346, or equivalent experience.
Equipment Recommendation
A laptop (PC or Mac) with full version of MATLAB® 5.0 (or later) installed. This will allow you to work the problems in class and do the practice “homework” problems. All of the problems will also be worked in class by the instructor, so this equipment is not required, but is recommended. The course notes are searchable and you can take electronic notes with the Adobe® Acrobat®9 Reader we provide to you.
Materials You Will Keep
A CD-ROM or USB drive with a color copy of all course notes. Bringing a laptop to this class is highly recommended for taking notes using the Adobe® Acrobat® sticky notes feature; power access will be provided. A black and white hard copy of the course notes, printed 3 slides to a page. Introduction to Random Signals and Applied Kalman Filtering, 3rd edition, by R. Grover Brown and Patrick Hwang, John Wiley & Sons, Inc., 1996. (Note: This arrangement does not apply to on-site contracts. Any books for on-site group contracts are negotiated on a case by case basis.)
To register or for more information, Contact Carolyn McDonald at (703) 256-8900 or cmcdonald@navtechgps.com.
15