Machine Learning for Radar Signal Processing

1. Auflage 2026
ISBN: 978-1-394-33995-2
Verlag: John Wiley & Sons Inc

Buch, Englisch, 496 Seiten

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.

Autoren/Hrsg.

Gu, Yujie

Zhang, Yimin D.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung

Inhaltsverzeichnis

Contents

Chapter 1: Deep Algorithm Unrolling for Radar Waveform Design

Chapter 2: Model-Based Reinforcement Learning for ISAC

Chapter 3: Low-Resolution Processing with Deep Learning

Chapter 4: Reinforcement Learning for Intelligent Radar Target Detection

Chapter 5: Learning Approaches for Detection and Mapping in Autonomous Agents’ Networks

Chapter 6: Sparse Antenna Array Interpolation with Model-Based Machine Learning and Deep Frequency Attention Networks

Chapter 7: Tensorized Neural Networks in Array Signal Processing

Chapter 8: SAR Image Interpretation with Deep Neural Networks

Chapter 9: Data-driven Kalman Filter for Target Tracking

Chapter 10: Physics-aware Deep Learning Techniques for Radar-based Human Activity Recognition

Chapter 11: Data-driven Resource Allocation in Distributed Radar Network

Über Autor(innen)

YUJIE GU, PhD, is a Senior Radar Scientist at Aptiv Advanced Engineering Center, Agoura Hills, California. He is a Subject Editor-in-Chief of Electronics Letters, an Editor of Signal Processing, and an elected member of the Sensor Array and Multichannel (SAM) Technical Committee. He is a Fellow of IEEE, a Fellow of EURASIP, and a Fellow of SPIE.

YIMIN D. ZHANG, PhD, is an Associate Professor with the Department of Electrical and Computer Engineering at Temple University, Philadelphia, Pennsylvania. He is a Senior Area Editor of IEEE Transactions on Signal Processing. He is a Fellow of IEEE, a Fellow of SPIE, and a Distinguished Lecturer of the IEEE Signal Processing Society.