作者介紹
王學謙|責編:戚亞
王學謙，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.
目錄
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
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