基於聯合稀疏的信號檢測與恢復方法研究-英文版王學謙 9787302620006 【台灣高等教育出版社】

Name: 基於聯合稀疏的信號檢測與恢復方法研究-英文版 王學謙 9787302620006 【台灣高等教育出版社】
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書名：基於聯合稀疏的信號檢測與恢復方法研究-英文版
ISBN：9787302620006
出版社：清華大學
著編譯者：王學謙
頁數：120
所在地：中國大陸 *此為代購商品
書號：1597253
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內容簡介

本書圍繞聯合稀疏信號的檢測和恢復，主要研究了聯合稀疏信號的檢測方法及其檢測性能界限、聯合稀疏信號的恢復方法及其在雷達成像問題中的應用；介紹了基於局部最大勢檢驗的聯合稀疏信號檢測方法，分析了該方法在模擬數據、低比特量化數據、高斯和廣義高斯雜訊情形下的理論檢測性能。同時，介紹了一種基於前瞻基信號選擇和雙塊稀疏性的聯合稀疏信號恢復方法，並以多極化雷達成像為應用實例，介紹了聯合稀疏信號的恢復方法；通過改善雷達圖像中非零像素點的聚集程度和抑制目標區域外的能量泄露，提升了雷達的成像質量。本書可供從事通信、雷達等信號處理的研究人員參考、學習。

作者簡介

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

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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