Intro
Adaptive Radar Detection Model-Based, Data-Driven, and Hybrid Approaches
Contents
Preface
Acknowledgments
1 Model-Based Adaptive Radar Detection
1.1 Introduction to Radar Processing
1.1.1 Generalities and Basic Terminology of Coherent Radars
1.1.2 Array Processing and Space-Time Adaptive Processing
1.1.3 Target Detection and Performance Metrics
1.2 Unstructured Signal in White Noise
1.2.1 Old but Gold: Basic Signal Detection and the Energy Detector
1.2.2 The Neyman-Pearson Approach
1.2.3 Adaptive CFAR Detection
1.2.4 Correlated Signal Model in White Noise
1.3 Structured Signal in White Noise
1.3.1 Detection of a Structured Signal in White Noise and Matched Filter
1.3.2 Generalized Likelihood Ratio Test
1.3.3 Detection of an Unknown Rank-One Signal in White Noise
1.3.4 Steering Vector Known up to a Parameter and Doppler Processing
1.4 Adaptive Detection in Colored Noise
1.4.1 One-Step, Two-Step, and Decoupled Processing
1.4.2 General Hypothesis Testing Problem via GLRT: A Comparison
1.4.3 Behavior under Mismatched Conditions: Robustness vs Selectivity
1.4.4 Model-Based Design of Adaptive Detectors
1.5 Summary
References
2 Classification Problems and Data-Driven Tools
2.1 General Decision Problems and Classification
2.1.1 M-ary Decision Problems
2.1.2 Classifiers and Decision Regions
2.1.3 Binary Classification vs Radar Detection
2.1.4 Signal Representation and Universal Approximation
2.2 Learning Approaches and Classification Algorithms
2.2.1 Statistical Learning
2.2.2 Bias-Variance Trade-Off
2.3 Data-Driven Classifiers
2.3.1 k-Nearest Neighbors
2.3.2 Linear Methods for Dimensionality Reduction and Classification
2.3.3 Support Vector Machine and Kernel Methods
2.3.4 Decision Trees and Random Forests.

2.3.5 Other Machine Learning Tools
2.4 Neural Networks and Deep Learning
2.4.1 Multilayer Perceptron
2.4.2 Feature Engineering vs Feature Learning
2.4.3 Deep Learning
2.5 Summary
References
3 Radar Applications of Machine Learning
3.1 Data-Driven Radar Applications
3.2 Classification of Communication and Radar Signals
3.2.1 Automatic Modulation Recognition and Physical-Layer Applications
3.2.2 Datasets and Experimentation
3.2.3 Classification of Radar Signals and Radiation Sources
3.3 Detection Based on Supervised Machine Learning
3.3.1 SVM-Based Detection with Controlled PFA
3.3.2 Decision Tree-Based Detection with Controlled PFA
3.3.3 Revisiting the Neyman-Pearson Approach
3.3.4 SVM and NN for CFAR Processing
3.3.5 Feature Spaces with (Generalized) CFAR Property
3.3.6 Deep Learning Based Detection
3.4 Other Approaches
3.4.1 Unsupervised Learning and Anomaly Detection
3.4.2 Reinforcement Learning
3.5 Summary
References
4 Hybrid Model-Based and Data-Driven Detection
4.1 Concept Drift, Retraining, and Adaptiveness
4.2 Hybridization Approaches
4.2.1 Different Dimensions of Hybridization
4.2.2 Hybrid Model-Based and Data-Driven Ideas in Signal Processing and Communications
4.3 Feature Spaces Based onWell-Known Statistics or Raw Data
4.3.1 Nonparametric Learning: k-Nearest Neighbor
4.3.2 Quasi-Whitened Raw Data as Feature Vector
4.3.3 Well-Known CFAR Statistics as a Feature Vector
4.4 Rethinking Model-Based Detection in a CFAR Feature Space
4.4.1 Maximal Invariant Feature Space
4.4.2 Characterizing Model-Based Detectors in CFAR-FP
4.4.3 Design Strategies in the CFAR-FP
4.5 Summary
References
5 Theories, Interpretability, and Other Open Issues
5.1 Challenges in Machine Learning
5.2 Theories for (Deep) Neural Networks.

5.2.1 Network Structures and Unrolling
5.2.2 Information Theory, Coding, and Sparse Representation
5.2.3 Universal Mapping, Expressiveness, and Generalization
5.2.4 Overparametrized Interpolation, Reproducing Kernel Hilbert Spaces, and Double Descent
5.2.5 Mathematics of Deep Learning, Statistical Mechanics, and Signal Processing
5.3 Open Issues
5.3.1 Adversarial Attacks
5.3.2 Stability, Efficiency, and Interpretability
5.3.3 Visualization
5.3.4 Sustainability, Marginal Return, and Patentability
5.4 Summary
References
List of Acronyms
List of Symbols
About the Author
Index.