Adaptive Radar Detection

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Coluccia, Angelo
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Norwood : Artech House, 2022.
Temas:
Adaptive signal processing.
Traitement adaptatif du signal.
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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