【新书推荐】【2020】雷达信号处理中的压缩感知

了解使用压缩感知工具和技术在雷达信号处理方面的最新理论和实践进展。

"Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing.

提供了一个广泛的视角，充分展示了这些工具的影响，可访问的教程般的章节涵盖了相关的主题，如杂波抑制，恒虚警检测，自适应波束形成，雷达随机阵列，空时自适应处理和MIMO雷达。

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.

每一章包括理论原理的覆盖、当前知识的详细回顾、关键应用的讨论，还强调了使用压缩感知算法的潜在好处。

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.

统一的符号可以在许多章节之间交叉引用，使其很容易探索不同的主题。

A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side.

由来自学术界和工业界的顶尖专家撰写，是从事信号处理和雷达系统的研究人员、研究生和行业专业人士的理想书本。”

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 :
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.

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标签：via,based,sparsity,信号处理,radar,CFAR,新书,2020,sensing
来源： https://blog.csdn.net/weixin_42825609/article/details/117452319