DOI码：10.1016/j.dsp.2015.12.006

发表刊物：Digital Signal Processing

关键字：1-Bit quantization, Compressive sensing, Sparse Bayesian learning, Variational message passing.

摘要：In a compressive sensing (CS) framework, a sparse signal can be stably reconstructed at a reduced sampling rate. Quantization and noise corruption are inevitable in practical applications. Recent studies have shown that using only the sign information of measurements can achieve accurate signal reconstruction in a CS framework. We consider the problem of reconstructing a sparse signal from 1-bit quantized, Gaussian noise corrupted measurements. In this paper, we present a variational Bayesian inference based 1-bit compressive sensing algorithm, which essentially models the effect of quantization as well as the Gaussian noise. A variational message passing method is adopted to achieve the inference. Through numerical experiments, we demonstrate that our algorithm outperforms state-of-the-art 1-bit compressive sensing algorithms in the presence of Gaussian noise corruption.

合写作者：Zhida Zhang

第一作者：Chongbin Zhou (周崇彬)

论文类型：期刊论文

通讯作者：Falin Liu

学科门类：工学

文献类型：J

卷号：50

页面范围：84-92

是否译文：否

发表时间：2016-03-01