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Novel deep learning models are achieving state-of-the-art accuracy in the area of radar target recognition, sometimes exceeding human-level performance. The book provides an introduction to the key aspects of machine learning that any radar engineer seeking to apply deep learning to radar signal processing ought to be aware of.
List of contents
- Prologue: perspectives on deep learning of RF data
- Part I: Fundamentals
- Chapter 1: Radar systems, signals, and phenomenology
- Chapter 2: Basic principles of machine learning
- Chapter 3: Theoretical foundations of deep learning
- Part II: Special topics
- Chapter 4: Radar data representation for classification of activities of daily living
- Chapter 5: Challenges in training DNNs for classification of radar micro-Doppler signatures
- Chapter 6: Machine learning techniques for SAR data augmentation
- Part III: Applications
- Chapter 7: Classifying micro-Doppler signatures using deep convolutional neural networks
- Chapter 8: Deep neural network design for SAR/ISAR-based automatic target recognition
- Chapter 9: Deep learning for passive synthetic aperture radar imaging
- Chapter 10: Fusion of deep representations in multistatic radar networks
- Chapter 11: Application of deep learning to radar remote sensing
- Epilogue: looking toward the future
Summary
Novel deep learning models are achieving state-of-the-art accuracy in the area of radar target recognition, sometimes exceeding human-level performance. The book provides an introduction to the key aspects of machine learning that any radar engineer seeking to apply deep learning to radar signal processing ought to be aware of.
