基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知

doi:10.3969/j.issn.1001-506X.2020.12.05

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (12): 2700-2707.doi: 10.3969/j.issn.1001-506X.2020.12.05

基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知

司菁菁^1,²(), 韩亚男¹(), 张磊¹(), 程银波³()

1. 燕山大学信息科学与工程学院, 河北秦皇岛 066004
2. 河北省信息传输与信号处理重点实验室, 河北秦皇岛 066004
3. 河北农业大学海洋学院, 河北秦皇岛 066003

收稿日期:2019-10-11 出版日期:2020-12-01 发布日期:2020-11-27
作者简介:司菁菁(1980-),女,副教授,博士,主要研究方向为非线性压缩感知、图像重构。E-mail:sjj@ysu.edu.cn|韩亚男(1994-),女,硕士研究生,主要研究方向为压缩感知。E-mail:1441933793@qq.com|张磊(1993-),男,硕士研究生,主要研究方向为压缩感知。E-mail:1143161762@qq.com|程银波(1978-),男,副教授,博士,主要研究方向为机器学习。E-mail:cyb@hebau.edu.cn
基金资助:
国家自然科学基金(61701429);河北省自然科学基金(F2018203137)

1-Bit compressive sensing based on generalized pattern-coupled sparse Bayesian learning

Jingjing SI^1,²(), Yanan HAN¹(), Lei ZHANG¹(), Yinbo CHENG³()

1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2. Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China
3. Ocean College, Hebei Agricultural University, Qinhuangdao 066003, China

Received:2019-10-11 Online:2020-12-01 Published:2020-11-27

摘要/Abstract

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。

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

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.

Key words: 1-Bit compressive sensing (CS), generalized sparse Bayesian learning (Gr-SBL), pattern-coupled, adaptive threshold

中图分类号:

TN911.73

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.

Jingjing SI, Yanan HAN, Lei ZHANG, Yinbo CHENG. 1-Bit compressive sensing based on generalized pattern-coupled sparse Bayesian learning[J]. Systems Engineering and Electronics, 2020, 42(12): 2700-2707.

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基于广义模式耦合稀疏Bayesian学习的1-Bit压缩感知

1-Bit compressive sensing based on generalized pattern-coupled sparse Bayesian learning

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