Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation

被引：5
|
作者：
Zhu, Jiang [1 ]
Zhang, Qi [1 ]
Meng, Xiangming [2 ]
Xu, Zhiwei [1 ]
机构：
[1] Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
[2] Huawei Technol Co Ltd, Shanghai 201206, Peoples R China
来源：
SIGNAL PROCESSING | 2020年 / 166卷
基金：
中国国家自然科学基金;
关键词：
VAMP; Structured perturbation; Compressed sensing; MAXIMUM-LIKELIHOOD; SIGN MEASUREMENTS; RECOVERY;
D O I：
10.1016/j.sigpro.2019.107248
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknown calibration parameters during the measurement process. Particularly, the sensing matrix may have some structure, which makes the perturbation follow a fixed pattern. Previous work has focused on extending the approximate message passing (AMP) and LASSO algorithm to deal with the independent and identically distributed (i.i.d.) perturbation. Based on the recent VAMP algorithm, we take the structured perturbation into account and propose the perturbation considered vector approximate message passing (PC-VAMP) algorithm. Numerical results demonstrate the effectiveness of PC-VAMP. (C) 2019 Elsevier B.V. All rights reserved.
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收藏
页数：5
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