| Summary: | The book has 11 chapters including a Prologue: perspectives on deep learning of RF data and an Epilogue: looking toward the future; and is divided into 3 parts. The first part deals with Fundamentals and covers the following topics: Radar systems, signals, and phenomenology; Basic principles of machine learning; and Theoretical foundations of deep learning. The second part covers Special topics and following topics are dealt with: Radar data representation for classification of activities of daily living; Challenges in training DNNs for classification of radar micro-Doppler signatures; and Machine learning techniques for SAR data augmentation. The third part deals with Applications and covers the following topics: Classifying micro-Doppler signatures using deep convolutional neural networks; Deep neural network design for SAR/ISAR-based automatic target recognition; Deep learning for passive synthetic aperture radar imaging; Fusion of deep representations in multistatic radar networks; and Application of deep learning to radar remote sensing.
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