本书围绕联合稀疏信号的检测和恢复，主要研究了联合稀疏信号的检测方法及其检测性能界限、联合稀疏信号的恢复方法及其在雷达成像问题中的应用；介绍了基于局部最大势检验的联合稀疏信号检测方法，分析了该方法在模拟数据、低比特量化数据、高斯和广义高斯噪声情形下的理论检测性能。同时，介绍了一种基于前瞻基信号选择和双块稀疏性的联合稀疏信号恢复方法，并以多极化雷达成像为应用实例，介绍了联合稀疏信号的恢复方法；通过改善雷达图像中非零像素点的聚集程度和抑制目标区域外的能量泄露，提升了雷达的成像质量。
本书可供从事通信、雷达等信号处理的研究人员参考、学习。

王学谦，Xueqian Wang received the B.S. and Ph.D. degrees in Electronic Engineering fromthe University of Electronic Science and Technology of China, Chengdu, China, in2015, and Tsinghua University, Beijing, China, in 2020, both with the highest honors.From 2018 to2019, he visited Syracuse University, Syracuse,NY,USA. From 2020 to2022, he was a Post-Doctoral Fellow with the Department of Electronic Engineering,Tsinghua University, Beijing, China. He is currently an Assistant Professor withthe Department of Electronic Engineering, Tsinghua University. His main researchinterests include target detection, information fusion, remote sensing, radar imaging,and distributed signal processing.
He has authored or coauthored 50 journal and conference papers. He is an IEEEMember and a reviewer for IEEE Transactions on Geoscience and Remote Sensing,IEEE Transactions on Signal Processing, IEEE Transactions on Communications,IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, and so on. TheDoctoral Thesis of Xueqian Wang has received the award of "Excellent DoctoralThesis of China Education Society of Electronics" and "Excellent Doctoral Thesisof Tsinghua University". He has received the awards of 2020 Postdoctoral Inno-vative Talent Support Program, 2020 Outstanding Graduate of Beijing, and 2022Outstanding Postdoctoral Fellow of Tsinghua University.

1 Introduction
1.1 Background
1.2 Related Works
1.2.1 Detection Methods for Jointly Sparse Signals
1.2.2 Recovery Methods for Jointly Sparse Signals
1.3 Main Content and Organization
References
2 Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Gaussian Noise
2.1 Introduction
2.2 Signal Model for Jointly Sparse Signal Detection
2.3 LMPT Detection Based on Analog Data
2.3.1 Detection Method
2.3.2 Theoretical Analysis of Detection Performance
2.4 LMPT Detection Based on Coarsely Quantized Data
2.4.1 Detection Method
2.4.2 Quantizer Design and the Effect of Quantization on Detection Performance
2.5 Simulation Results
2.5.1 Simulation Results of the LMPT Detector with Analog Data
2.5.2 Simulation Results of the LMPT Detector with Quantized Data
2.6 Conclusion
References
3 Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Generalized Gaussian Model
3.1 Introduction
3.2 The LMPT Detector Based on Generalized Gaussian Model and Its Detection Performance
3.2.1 Generalized Gaussian Model
3.2.2 Signal Detection Method
3.2.3 Theoretical Analysis of Detection Performance
3.3 Quantizer Design and Analysis of Asymptotic Relative Efficiency
3.3.1 Quantizer Design
3.3.2 Asymptotic Relative Ef?ciency
3.4 Simulation Results
3.5 Conclusion
References
4 Jointly Sparse Signal Recovery Method Based on Look-Ahead-Atom-Selection
4.1 Introduction
4.2 Background of Recovery of Jointly Sparse Signals
4.3 Signal Recovery Method Based on Look-Ahead-Atom-Selection and Its Performance Analysis
4.3.1 Signal Recovery Method
4.3.2 Performance Analysis
4.4 Experimental Results
4.5 Conclusion
References
5 Signal Recovery Methods Based on Two-Level Block Sparsity
5.1 Introduction
5.2 Signal Recovery Method Based on Two-Level Block Sparsity with Analog Measurements
5.2.1 PGM-Based Two-Level Block Sparsity
5.2.2 Two-Level Block Matching Pursuit
5.3 Signal Recovery Method Based on Two-Level Block Sparsity with 1-Bit Measurements
5.3.1 Background of Sparse Signal Recovery Based on 1-Bit Measurements
5.3.2 Enhanced-Binary Iterative Hard Thresholding
5.4 Simulated and Experimental Results
5.4.1 Simulated and Experimental Results Based on Analog Data
5.4.2 Simulated and Experimental Results Based on 1-Bit Data
5.5 Conclusion
References
6 Summary and Perspectives
6.1 Summary
6.2 Perspectives
References
Appendix A: Proof of (2.61)
Appendix B: Proof of Lemma 1
Appendix C: Proof of (3.6)
Appendix D: Proof of Theorem 1
Appendix E: Proof of Lemma 2
About the Author