Robust 1-bit Compressive Sensing via Variational Bayesian Algorithm

DOI number:10.1016/j.dsp.2015.12.006

Journal:Digital Signal Processing

Key Words:1-Bit quantization, Compressive sensing, Sparse Bayesian learning, Variational message passing.

Abstract: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.

First Author:Chongbin Zhou (周崇彬)

Co-author:Zhida Zhang

Indexed by:Journal paper

Correspondence Author:Falin Liu

Discipline:Engineering

Document Type:J

Volume:50

Page Number:84-92

Translation or Not:no

Date of Publication:2016-03-01