Compressed Sensing in Radar Signal Processing

Learn about the latest theoretical and practical advances in radar signal processing using tools from compressive sensing.

Antonio De Maio (Edited by), Yonina C. Eldar (Edited by), Alexander M. Haimovich (Edited by)

9781108428293, Cambridge University Press

Hardback, published 17 October 2019

378 pages, 235 b/w illus.
25.3 x 17.8 x 2.1 cm, 0.89 kg

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.

Preface Antonio De Maio, Yonina C. Eldar and Alexander M. Haimovich
1. Sub-Nyquist radar: principles and prototypes Kumar Vijay Mishra and Yonina C. Eldar
2. Clutter rejection and adaptive filtering in compressed sensing radar Peter B. Tuuk
3. RFI mitigation based on compressive sensing methods for UWB radar imaging Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston and Lam H. Nguyen
4. Compressed CFAR techniques Laura Anitori and Arian Maleki
5. Sparsity-based methods for CFAR target detection in STAP random arrays Haley H. Kim and Alexander M. Haimovich
6. Fast and robust sparsity-based STAP method for nonhomogeneous clutter Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long and Tapan K. Sarkar
7. Super-resolution radar imaging via convex optimization Reinhard Heckel
8. Adaptive beamforming via sparsity-based reconstruction of covariance matrix Yujie Gu, Nathan A. Goodman and Yimin D. Zhang
9. Spectrum sensing for cognitive radar via model sparsity exploitation Augusto Aubry, Vincenzo Carotenuto, Antonio De Maio and Mark Govoni
10. Cooperative spectrum sharing between sparse-sensing-based radar and communication systems Bo Li and Athina P. Petropulu
11. Compressed sensing methods for radar imaging in the presence of phase errors and moving objects Ahmed Shaharyar Khwaja, Naime Ozben Onhon and Mujdat Cetin.

Subject Areas: Signal processing [UYS], Computer science [UY], Computer networking & communications [UT], Radar [TJKD], Communications engineering / telecommunications [TJK], Electronics & communications engineering [TJ]