Dr Theodoros G. Kostis CEng MIEE Defence against Stealth Aircraft using Cognitive Radar Strategies

Cryptology: Methods & Applications

Defence against Stealth Aircraft using Cognitive Radar Strategies Dr Theodoros G. Kostis CEng MIEE 2nd CryptAAF International Conference on Cryptography, Network Security & Applications in the Armed Forces Vari, HELLAS, 2nd APRIL 2014.

HELLENIC MILITARY ACADEMY, GREECE

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Presentation Outline

Phase 1. Introduction Phase 2. Analysis Phase 3. Conclusions

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Radar: Definition Radar (acronym for RAdio Detection And Ranging) is an object detection system that uses radio waves to determine the range, altitude, direction and speed of objects.

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The Radar Problem The Radar Problem A target signature is to be reconstructed then classified and ideally identified from limited in number and potential measurements of scattered electromagnetic fields.

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The Detection Problem Detection & Confusion Matrix A table that allows the visualization of the performance capacity of an algorithm or a process.

The characteristic radar confusion matrix 5

Cognitive Radar a solution?

Effective sensor operation within complex environments requires adaptation via • constant monitoring of interference, • cooperation with sensors of different capabilities and • optimized illumination waveforms for the particular real-time scanario.

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Goals & Means

Make an expert system that can utilize the radarcube data streams acquired by a phased array antenna in the most constructive manner towards the detection a faint targets 7

Presentation Outline

Phase 1. Introduction Phase 2. Analysis Phase 3. Conclusions

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Radarcube Coherent Radar

Principles of Modern Radar Vol.1 , Scitech Publishing.

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Phased Array Antenna

• Sy

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Faint Airborne Radar Target

The Wooden Wonder 11

Expert System Analysis In artificial intelligence, an expert system is a computer system that emulates the decisionmaking ability of a human expert.

Radar is a Hybrid System (Man-Machine)

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Process Description Remote sensing with radar is a linear inverse problem.

Unfortunately incomplete!

Y = XB + U, Where Y: measurements, X, design matrix consisting of independent variables that represent a statistical model, B, the parameters to be estimated (the target signature) and U: noise errors 13

Process Description Usual Practice : Do alert-confirm and continue to track-while-scan But if the target is very faint it is difficult to get confirmation from subsequent passes and declare a detection

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Process Description New Methodology : Since the measurement results are transformed into a sparse matrix 1. Perform doppler processing and ISAR and then 2. Extract the mutual information from every instance of the dwell time 3. Where mutual information is best perform Space-Time Adaptive Processing 4. Use the resulting sparse matrix to extract the eigenvalues 5. Recreate the corresponding eigenfunction 6. Use this eigenfunction to fine-tune the transmit signal 15

Process Description

How to get the eigenvalues?

Points after STAP that will remain in the same resolution cell will be the eigenvalues!

These will guide the beamforming process and adaptively tailor the pulse compression value of the transmit waveform.

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Presentation Outline

Phase 1. Introduction Phase 2. Analysis Phase 3. Conclusions

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Conclusions

Constructing the eigenvalue from the mutual information of the persistent instances of the dwell time allows the optimum adaptation of the transmit waveform to suite the particular target.

Radar Threshold not fully required!

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Thank you for your time!

Questions?

Defence against Stealth Aircraft using Cognitive Radar Strategies Dr Theodoros G. Kostis CEng MIEE {tkostis@iee.org} 19