Compressed Sensing in Radar Signal Processing
Published on 17. October 2019
Book
Hardback
378 pages
978-1-108-42829-3 (ISBN)
Description
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Laminated cover
Illustrations
Worked examples or Exercises; 18 Halftones, black and white; 217 Line drawings, black and white
Dimensions
Height: 255 mm
Width: 182 mm
Thickness: 22 mm
Weight
893 gr
ISBN-13
978-1-108-42829-3 (9781108428293)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Antonio De Maio | Yonina C. Eldar | Alexander M. Haimovich
Compressed Sensing in Radar Signal Processing
E-Book
10/2019
Cambridge University Press
€105.99
Available for download
Persons
Antonio De Maio is a Professor in the Department of Electrical Engineering and Information Technology at the Universita 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).
Content
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.