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

被引：0
|
作者：
Si J. [1 ,2 ]
Han Y. [1 ]
Zhang L. [1 ]
Cheng Y. [3 ]
机构：
[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao
[2] Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao
[3] Ocean College, Hebei Agricultural University, Qinhuangdao
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 12期
关键词：
1-Bit compressive sensing (CS); Adaptive threshold; Generalized sparse Bayesian learning (Gr-SBL); Pattern-coupled;
D O I：
10.3969/j.issn.1001-506X.2020.12.05
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2700 / 2707
页数：7
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