| Description |
1 online resource (235 p.) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Notes |
Description based upon print version of record |
|
4 Hybrid Model-Based and Data-Driven Detection |
| Subject |
Adaptive signal processing.
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| Genre/Form |
Electronic books
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| Form |
Electronic book
|
| ISBN |
9781630819019 |
|
1630819018 |
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