Compressed Sensing in Radar Signal Processing

Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.

Table of Contents

1 Sub-Nyquist Radar: Principles and Prototypes

2 Clutter Rejection and Adaptive Filtering in Compressed Sensing Radar

3 RFI Mitigation Based on Compressive Sensing Methods for UWB Radar Imaging

4 Compressed CFAR Techniques

5 Sparsity-Based Methods for CFAR Target Detection in STAP Random Arrays

6 Fast and Robust Sparsity-Based STAP Methods for Nonhomogeneous Clutter

7 Super-Resolution Radar Imaging via Convex Optimization

8 Adaptive Beamforming via Sparsity-Based Reconstruction of Covariance Matrix

9 Spectrum Sensing for Cognitive Radar via Model Sparsity Exploitation

10 Cooperative Spectrum Sharing between Sparse Sensing-Based Radar and Communication Systems

11 Compressed Sensing Methods for Radar Imaging in the Presence of Phase Errors and Moving Objects

About the Authors

Antonio De Maio is a Professor in the Department of Electrical Engineering and Information Technology at the Università degli Studi di Napoli Federico II, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Yonina C. Eldar is a Professor at the Weizmann Institute of Science. She has authored and edited several books, including Sampling Theory: Beyond Bandlimited Systems (Cambridge, 2015) and Compressed Sensing: Theory and Applications (Cambridge, 2012). She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and Eurasip, and a member of the Israel National Academy of Science and Humanities.

Alexander M. Haimovich is a Distinguished Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE).