Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing
Information Theory
2018-05-09 v1 math.IT
Abstract
In this letter, a binary sparse Bayesian learning (BSBL) algorithm is proposed to slove the one-bit compressed sensing (CS) problem in both single measurement vector (SMV) and multiple measurement vectors (MMVs). By utilising the Bussgang-like decomposition, the one-bit CS problem can be approximated as a standard linear model. Consequently, the standard SBL algorithm can be naturally incorporated. Numerical results demonstrate the effectiveness of the BSBL algorithm.
Cite
@article{arxiv.1805.03043,
title = {Binary Sparse Bayesian Learning Algorithm for One-bit Compressed Sensing},
author = {Jiang Zhu and Lin Han and Xiangming Meng and Zhiwei Xu},
journal= {arXiv preprint arXiv:1805.03043},
year = {2018}
}