Compressed Sensing in Radar Signal Processing (eBook, PDF)

Redaktion: Maio, Antonio De

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 17. Oktober 2019
• Englisch
• ISBN-13: 9781108559072
• Artikelnr.: 66178471

Inhaltsangabe

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.