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

Abstract

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

CLC Number:

TN911.73

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.

Figures/Tables 12

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References 28

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

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