Next-Generation Cognitive Radar Systems

✍ Scribed by Kumar Vijay Mishra (editor), Bhavani Shankar M.R. (editor), Muralidhar Rangaswamy (editor)

• Publisher: Scitech Publishing
• Year: 2024
• Tongue: English
• Leaves: 685
• Series: Radar, Sonar and Navigation
• Category: Library

✦ Synopsis

Next-Generation Cognitive Radar Systems brings together contributions from leading researchers who are engaged in the research and development of next generation cognitive abilities in radar engineering. It features recent advances in the theory and applications of advanced Cognitive Radar (CR) tools and examines emerging challenges. The chapters include mathematical and computational methods to combat important CR challenges as well as the applications of recent theories and algorithms to various applied CR aspects.

The book is intended to be used as a supplementary text for first-level graduate courses on radar theory and systems, radar signal processing, detection and estimation theory, and array signal processing. The book can also be used as a main textbook for upper-level graduate courses such as advanced topics in electromagnetics, advanced topics in radar, rf and communications, and contemporary topics in signal processing and optimization.

✦ Table of Contents

Cover
Contents
About the editors
List of editors
List of contributors
List of reviewers
Preface
Acknowledgments
Part I Fundamentals
1 Beyond cognitive radar
1.1 Aspects of cognition
1.2 Key technology enablers
1.2.1 Convex and non-convex optimization
1.2.2 Control-theoretic tools
1.2.3 Learning techniques
1.2.4 Operationalization
1.3 Organization of the book
References
2 Adversarial radar inference: inverse tracking, identifying cognition, and designing smart interference
2.1 Introduction
2.1.1 Objectives
2.1.2 Perspective
2.1.3 Organization
2.2 Inverse tracking and estimating adversary’s sensor
2.2.1 Background and preliminary work
2.2.2 Inverse tracking algorithms
Example: inverse Kalman filter
2.2.3 Estimating the adversary’s sensor gain
2.2.4 Example. Estimating adversary’s gain in linear Gaussian case
2.3 Identifying utility maximization in a cognitive radar
2.3.1 Background. Revealed preferences and Afriat’s theorem
2.3.2 Beam allocation: revealed preference test
2.3.3 Waveform adaptation: revealed preference test for non-linear budgets
2.4 Designing smart interference to confuse cognitive radar
2.4.1 Interference signal model
2.4.2 Smart interference for confusing the radar
2.4.3 Numerical example illustrating design of smart interference
2.5 Stochastic gradient-based iterative smart interference
2.5.1 Smart interference with measurement noise
2.5.2 Algorithms for solving constrained optimization problem (2.41)
Acknowledgment
References
3 Information integration from human and sensing data for cognitive radar
3.1 Integration of human decisions with physical sensors in binary hypothesis testing
3.1.1 Decision fusion for physical sensors and human sensors
3.1.2 Asymptotic system performance when humans possess side information
3.2 Prospect theoretic utility-based human decision making in multi-agent systems
3.2.1 Subjective utility-based hypothesis testing
3.2.2 Decision fusion involving human participation
3.3 Human–machine collaboration for binary decision-making under correlated observations
3.3.1 Human–machine collaboration model
3.3.2 Copula-based decision fusion at the FC
3.3.3 Performance evaluation
3.4 Current challenges in human–machine teaming
3.5 Summary
References
4
Channel estimation for cognitive fully adaptive radar
4.1 Introduction
4.2 Traditional covariance-based statistical model
4.3 Stochastic transfer function model
4.4 Cognitive radar framework
4.5 Unconstrained channel estimation algorithms
4.5.1 SISO/SIMO channel estimation
4.5.2 MIMO channel estimation
4.5.3 Minimal probing strategies
4.6 Constrained channel estimation algorithm
4.6.1 Cosine similarity measurement
4.6.2 Channel estimation under the cosine similarity constraint: non-convex QCQP
4.6.3 Performance comparison using numerical simulation
4.7 Cognitive fully adaptive radar challenge dataset
4.7.1 Scenario 1
4.7.2 Scenario 2
4.8 Concluding remarks
References
5
Convex optimization for cognitive radar
5.1 Introduction
5.1.1 Waveform design problems in cognitive radar
5.2 Background and motivation
5.2.1 Principles of convex optimization
5.2.2 Challenges of optimization problems for cognitive radar
5.3 Constrained optimization for cognitive radar
5.3.1 SINR maximization
5.3.2 Spatio-spectral radar beampattern design
5.3.3 Quartic gradient descent for tractable radar ambiguity function shaping
5.4 Summary
References
Part II
Design methodologies
6
Cognition-enabled waveform design for ambiguity function shaping
6.1 Introduction
6.2 Preliminaries to AF and optimization methods
6.2.1 Ambiguity function and its shaping
6.2.2 MM and Dinkelbach’s algorithm
6.3 Waveform design for AF shaping via SINR maximization
6.3.1 System model and problem formulation
6.3.2 Waveform design via MM
6.3.3 Convergence analysis and accelerations
6.3.4 Numerical experiments
6.4 Waveform design via minimization of regularized spectral level ratio
6.4.1 Regularized SLR and problem formulation
6.4.2 Approximate iterative method for spectrum shaping
6.4.3 Monotonic iterative method for spectrum shaping
6.4.4 Numerical experiments
6.5 Conclusions
Appendix
A.1 Proof of Lemma 2
A.2 Proof of Lemma 4
A.3 Proof of Lemma 5
A.4 Proof of Lemma 6
A.5 Proof of Lemma 8
A.6 Proof of Lemma 9
References
7
Training-based adaptive transmit–receive beamforming for MIMO radars
7.1 Introduction
7.1.1 Background
7.1.2 Contributions
7.2 System model
7.2.1 Target contribution
7.2.2 Clutter contribution
7.2.3 Noise model
7.3 Adaptive beamforming
7.3.1 Receive beamforming
7.3.2 Transmit beamforming: known covariance
7.3.3 Transmit BF: estimating the required covariance matrix
7.4 Reduced-dimension transmit beamforming
7.5 Transmit BF for multiple Doppler bins
7.6 Numerical results
7.6.1 Random phase radar signals
7.6.2 Airborne radar
7.7 Conclusion
Acknowledgment
References
8
Random projections and sparse techniques in radar
8.1 Introduction
8.2 A critical perspective on sub-sampling claims in compressive sensing theory
8.2.1 General issues of non-stationarity
8.2.2 Sparse signal in intermediate frequency (IF)
8.2.3 Temporally sparse signal in baseband
8.3 Random projections STAP model
8.3.1 Computational complexity and a “small” data problem
8.3.2 Random projections
8.3.3 Localized random projections
8.3.4 Semi-random localized projection
8.4 Statistical analysis
8.4.1 Probabilistic bounds
8.5 Simulations
8.5.1 Integration as low-pass filtering
8.5.2 CS: sinusoid in IF example
8.5.3 CS: rectangular pulse example
8.5.4 Realistic examples of CS reconstructions
8.5.5 Random projections with different distributions
8.5.6 Random and random-type projections
8.6 Discussion and conclusions
Acknowledgment
References
9
Fully adaptive radar resource allocation for tracking and classification
9.1 Introduction
9.2 Fully adaptive radar framework
9.3 Multitarget multitask FARRA system model
9.3.1 Radar resource allocation model
9.3.2 Controllable parameters
9.3.3 State vector
9.3.4 Transition model
9.3.5 Measurement model
9.4 FARRA PAC
9.4.1 Perceptual processor
9.4.2 Executive processor
9.5 Simulation results
9.6 Experimental results
9.7 Conclusion
Acknowledgment
References
10
Stochastic control for cognitive radar
10.1 Introduction
10.2 Connection to earlier work
10.3 Stochastic optimization framework
10.3.1 General problem components
10.3.2 Partial observability
10.4 Objective functions for cognitive radar
10.4.1 Task-based reward functions
10.4.2 Information theoretic reward functions
10.4.3 Utility and QoS-based objective functions
10.5 Multi-step objective function
10.5.1 Optimal values and policies
10.5.2 Simplified multi-step objective functions
10.6 Policies and perception–action cycles
10.6.1 Policy search
10.6.2 Lookahead approximations
10.6.3 Discussion
10.7 Relationship between cognitive radar and stochastic optimization
10.7.1 Problem components
10.7.2 Typical cognitive radar solution methodologies
10.7.3 Cognitive radar objective functions
10.8 Simulation examples
10.8.1 Adaptive tracking example
10.8.2 Target resource allocation example
10.9 Conclusion
References
11
Applications of game theory in cognitive radar
11.1 Introduction
11.1.1 Research background
11.1.2 Literature review
11.1.3 Motivation
11.1.4 Major contributions
11.1.5 Outline of the chapter
11.2 System and signal models
11.2.1 System model
11.2.2 Signal model
11.3 Game theoretic formulation
11.3.1 Feasible extension
11.4 Existence and uniqueness of the Nash equilibrium
11.4.1 Existence
11.4.2 Uniqueness
11.5 Iterative power allocation method
11.6 Simulation results and performance evaluation
11.6.1 Parameter designation
11.6.2 Numerical results
11.7 Conclusion
References
12
The role of neural networks in cognitive radar
12.1 Cognitive process modeling with neural networks
12.1.1 Background and motivation
12.1.2 Situation awareness and connection to perception–action cycle
12.1.3 Memory and attention
12.1.4 Knowledge representation
12.1.5 A three-layer cognitive architecture
12.1.6 Applications of machine learning in a cognitive radar architecture
12.2 Integration of domain knowledge via physics-aware DL
12.2.1 Physics-aware DNN training using synthetic data
12.2.2 Adversarial learning for initialization of DNNs
12.2.3 Generative models and their kinematic fidelity
12.2.4 Physics-aware DNN design
12.2.5 Addressing temporal dependencies in time-series data
12.3 Reinforcement learning
12.3.1 Overview
12.3.2 Basics of reinforcement learning
12.3.3 Q-Learning algorithm
12.3.4 Deep Q-network algorithm
12.3.5 Deep deterministic policy gradient algorithm
12.3.6 Algorithm selection
12.3.7 Example reinforcement learning implementation
12.3.8 Cautionary topics
12.3.9 Angular action spaces
12.3.10 Accuracy of environment during training
12.4 End-to-end learning for jointly optimizing data to decision pipeline
12.4.1 End-to-end learning architecture
12.4.2 Loss function of the end-to-end architecture
12.4.3 Simulation results
12.5 Conclusion
Acknowledgments
References
Part III
Beyond cognitive radar—from theory to practice
13
One-bit cognitive radar
13.1 Introduction
13.2 System model
13.3 Bussgang-theorem-aided estimation
13.4 Radar processing for stationary targets
13.4.1 Estimation of stationary target parameters
13.4.2 Time-varying threshold design
13.5 Radar processing for moving targets
13.5.1 Problem formulation for moving targets
13.5.2 Estimation of moving target parameters
13.6 Other low-resolution sampling scenarios
13.6.1 Extension to parallel one-bit comparators
13.6.2 Extension to p-bit ADCs
13.7 Numerical analysis for one-bit radar signal processing
13.7.1 Stationary targets
13.7.2 Moving targets
13.8 One-bit radar waveform design under uncertain statistics
13.8.1 Problem formulation for waveform design
13.8.2 Joint design method: CREW (one-bit)
13.9 Waveform design examples
13.10 Concluding remarks
References
14
Cognitive radar and spectrum sharing
14.1 The spectrum problem
14.1.1 Introduction
14.1.2 Spectrum and spectrum allocation
14.1.3 Cognitive radar definition
14.1.4 Target-matched illumination
14.1.5 Embedded communications
14.1.6 Low probability of intercept (LPI)
14.1.7 Summary
14.2 Joint radar and communications research
14.2.1 Applications of joint radar and communication
14.2.2 Co-existence radar and communication research
14.2.3 Single waveform tasked with both radar and communication
14.2.4 LPI radar and communication waveforms
14.2.5 Adaptive/cognitive radar concepts and examples
14.3 Summary and conclusions
Acknowledgments
References
15
Cognition in automotive radars
15.1 Introduction
15.2 Review of automotive radar
15.2.1 Automotive radar
15.2.2 FMCW radar
15.2.3 MIMO radar and angle estimation
15.3 Cognitive radar
15.3.1 Perception–action cycle
15.3.2 Perception
15.3.3 Learning
15.3.4 Action
15.4 Physical environment perception for FMCW automotive radars
15.4.1 Range–velocity imaging
15.4.2 Micro-Doppler imaging
15.4.3 Range–angle imaging
15.4.4 Synthetic aperture radar imaging
15.4.5 Radar object recognition based on radar image
15.5 Cognitive spectrum sharing in automotive radar network
15.5.1 Spectrum congestion, interference issue, and MAC schemes
15.5.2 FMCW-CSMA-based spectrum sharing
15.5.3 FMCW-cognitive-CSMA-based spectrum sharing
15.5.4 Comments on spectrum sharing for cognitive radar
15.6 Concluding remarks
References
16 A canonical cognitive radar architecture
16.1 A canonical CR architecture
16.2 Full transmit–receive adaptivity
16.2.1 Full transmit adaptivity
16.2.2 Full receive adaptivity
16.3 CR real-time channel estimation (RTCE)
16.4 CR radar scheduler
16.5 Cognitive radar and artificial intelligence
16.6 Implementation considerations
16.7 Advanced modeling and simulation to support cognitive radar
16.8 Remaining challenges and areas for future research
References
17
Advances in cognitive radar experiments
17.1 The need for cognitive radar experiments
17.1.1 Cognition for radar sensing
17.1.2 Chapter overview
17.2 The CREW test bed
17.2.1 The CREW design
17.2.2 CREW demonstration experiments
17.3 The cognitive detection, identification, and ranging testbed
17.3.1 Development considerations
17.3.2 The CODIR design
17.3.3 Experimental work with CODIR
17.4 Universal software radio peripheral-based cognitive radar testbed
17.4.1 USRP testbed design
17.4.2 USRP testbed demonstration experiments
17.5 The miniature cognitive detection, identification, and ranging testbed
17.5.1 The miniCODIR design
17.5.2 miniCODIR experiments
17.6 Other cognitive radar testbeds
17.6.1 SDRadar: cognitive radar for spectrum sharing
17.6.2 Spectral coexistence via xampling (SpeCX)
17.6.3 Anticipation in NetRad
17.7 Future cognitive radar testbed considerations
17.7.1 Distributed cognitive radar systems
17.7.2 Machine learning techniques
17.7.3 Confluence of algorithms—metacognition
17.8 Summary
Acknowledgments
References
18
Quantum radar and cognition: looking for a potential cross fertilization
18.1 Introduction
18.2 Cognitive radar
18.2.1 Cognitive radar scheduler
18.2.2 Within the cognitive radar
18.2.3 Verification and validation
18.3 Quantum mechanics in a nutshell
18.4 Quantum harmonic oscillator
18.5 Quantum electromagnetic field
18.5.1 Single mode
18.5.2 Multiple modes
18.6 Quantum illumination
18.7 An experimental demonstration
18.8 Hybridization of cognitive and quantum radar: what recent research in neuroscience can tell about
18.9 Quantum and cognitive radar
18.10 Conclusions
Acknowledgments
References
19
Metacognitive radar
19.1 Metacognitive concepts in radar
19.1.1 Metacognitive cycle
19.1.2 Applications: metacognitive spectrum sharing
19.1.3 Applications: metacognitive power allocation
19.1.4 Applications: Metacognitive antenna selection
19.2 Cognition masking
19.3 Example: antenna selection across geometries
19.3.1 Cognitive cycle
19.3.2 Knowledge transfer across different array geometries
19.4 Numerical simulations
19.5 Summary
References
Epilogue
Index
Back Cover