| 基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知 |
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| 引用本文： | 司菁菁,韩亚男,张磊,程银波. 基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知[J]. 系统工程与电子技术, 2020, 42(12): 2700-2707. DOI: 10.3969/j.issn.1001-506X.2020.12.05 |
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| 作者姓名： | 司菁菁 韩亚男 张磊 程银波 |
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| 作者单位： | 1. 燕山大学信息科学与工程学院, 河北 秦皇岛 0660042. 河北省信息传输与信号处理重点实验室, 河北 秦皇岛 0660043. 河北农业大学海洋学院, 河北 秦皇岛 066003 |
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| 基金项目： | 国家自然科学基金(61701429);河北省自然科学基金(F2018203137) |
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| 摘 要： | 在1-Bit压缩感知(compressive sensing, CS)框架下,将信号的稀疏结构先验引入广义稀疏Bayesian学习(generalized sparse Bayesian learning, Gr-SBL),研究基于Gr-SBL的1-Bit CS重构。将广义线性模型与模式耦合稀疏Bayesian学习相结合,提出了一种基于广义模式耦合稀疏Bayesian学习1-Bit CS重构算法，简称为1-Bit Gr-PC-SBL算法。该算法将1-Bit CS重构问题迭代地分解成一系列标准CS重构问题,在信号稀疏模式未知的情况下,基于模式耦合稀疏Bayesian学习实现信号重构。进而,引入阈值自适应的二进制量化,设计了自适应阈值的1-Bit Gr-PC-SBL算法,进一步提升了算法的信号重构性能。
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| 关 键 词： | 1-Bit压缩感知 广义稀疏Bayesian学习 模式耦合 自适应阈值 |
| 收稿时间： | 2019-10-11 |
1-Bit compressive sensing based on generalized pattern-coupled sparse Bayesian learning |
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| Jingjing SI,Yanan HAN,Lei ZHANG,Yinbo CHENG. 1-Bit compressive sensing based on generalized pattern-coupled sparse Bayesian learning[J]. System Engineering and Electronics, 2020, 42(12): 2700-2707. DOI: 10.3969/j.issn.1001-506X.2020.12.05 |
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| Authors: | Jingjing SI Yanan HAN Lei ZHANG Yinbo CHENG |
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| Affiliation: | 1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China2. Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China3. Ocean College, Hebei Agricultural University, Qinhuangdao 066003, China |
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| Abstract: | Under the framework of 1-Bit compressive sensing (CS), the signal's sparsity structure prior is introduced into generalized sparse Bayesian learning (Gr-SBL), and the reconstruction of 1-Bit CS based on Gr-SBL is studed. The generalized linear models are combined with the pattern-coupled sparse Bayesian learning, and the 1-Bit CS reconstruction algorithm based on generalized pattern-coupled sparse Bayesian learning is proposed, which is shortened to 1-Bit Gr-PC-SBL algorithm. This algorithm iteratively reduces the 1-Bit CS reconstruction problem to a sequence of standard CS reconstruction problems, and realizes signal reconstruction based on pattern-coupled sparse Bayesian learning, while the signal's sparse patterns are entirely unknown. Furthermore, binary quantization with adaptive thresholds is introduced, and a 1-Bit Gr-PC-SBL algorithm with adaptive thresholds is proposed, which can further improve the reconstruction performance of the algorithm. |
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| Keywords: | 1-Bit compressive sensing (CS) generalized sparse Bayesian learning (Gr-SBL) pattern-coupled adaptive threshold |
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