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Compressed Sensing in Radar Signal Processing

Compressed Sensing in Radar Signal Processing

c.$120.00 ( )

Antonio De Maio, Yonina C. Eldar, Alexander M. Haimovich, Kumar Vijay Mishra, Peter B. Tuuk, Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston, Lam H. Nguyen, Laura Anitori, Arian Maleki, Haley H. Kim, Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long, Tapan K. Sarkar, Reinhard Heckel, Yujie Gu, Nathan A. Goodman, Yimin D. Zhang, Augusto Aubry, Vincenzo Carotenuto, Mark Govoni, Bo Li, Athina P. Petropulu, Ahmed Shaharyar Khwaja, Naime Ozben Onhon, Mujdat Cetin
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  • Publication planned for: December 2019
  • availability: Not yet published - available from December 2019
  • format: Hardback
  • isbn: 9781108428293

c.$ 120.00 ( )
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  • 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.

    • Covers both theoretical and practical advances in radar signal processing using compressed sensing
    • Provides broad coverage of topics, including clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar
    • Uses an accessible, tutorial-like approach
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    Product details

    • Publication planned for: December 2019
    • format: Hardback
    • isbn: 9781108428293
    • dimensions: 247 x 174 mm
    • availability: Not yet published - available from December 2019
  • Table of Contents

    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.

  • Editors

    Antonio De Maio, Università degli Studi di Napoli 'Federico II'
    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, Weizmann Institute of Science, Israel
    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, New Jersey Institute of Technology
    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).

    Contributors

    Antonio De Maio, Yonina C. Eldar, Alexander M. Haimovich, Kumar Vijay Mishra, Peter B. Tuuk, Tianyi Zhang, Jiaying Ren, Jian Li, David J. Greene, Jeremy A. Johnston, Lam H. Nguyen, Laura Anitori, Arian Maleki, Haley H. Kim, Xiaopeng Yang, Yuze Sun, Xuchen Wu, Teng Long, Tapan K. Sarkar, Reinhard Heckel, Yujie Gu, Nathan A. Goodman, Yimin D. Zhang, Augusto Aubry, Vincenzo Carotenuto, Mark Govoni, Bo Li, Athina P. Petropulu, Ahmed Shaharyar Khwaja, Naime Ozben Onhon, Mujdat Cetin

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