In-depth resource exploring the intersection of advanced machine learning techniques and radar signal processing Machine Learning for Radar Signal Processing provides a comprehensive guide to the latest research advancements and challenges in applying machine learning techniques to radar signal processing. It addresses data-driven methods tailored to radar applications, including waveform design, low-bit sampling, sparse sampling, and detection and mapping. It also covers techniques for array processing, direction-of-arrival (DOA) estimation and beamforming, synthetic aperture radar (SAR) image interpretation, robust filtering and tracking, gesture recognition, integrated sensing and communications (ISAC), and resource allocation. In addition, the book: Presents advanced machine learning methods and techniques to conduct research and development in radar signal processing and radar system integration Demonstrates a focus on real-world applications with chapters on SAR image interpretation and human activity recognition Provides the expertise of leading and highly respected researchers from around the world Considers future prospects in the field, with insights from a diverse range of experts Machine Learning for Radar Signal Processing is an essential resource for academics, scientists, and engineers engaged in the broad fields of radar, communication, and sonar applications. Readers are expected to have prerequisite knowledge in linear algebra, probability and statistics, control and optimization, foundational radar concepts, and fundamental machine learning principles.