Adaptive one-bit quantisation via approximate message passing with nearest neighbour sparsity pattern learning

被引：9
作者：
Cao, Hangting [1 ]
Zhu, Jiang [1 ]
Xu, Zhiwei [1 ]
机构：
[1] Zhejiang Univ, Ocean Coll, Key Lab Ocean Observat Imaging Testbed Zhejiang P, 1 Zheda Rd, Zhoushan 316021, Peoples R China
来源：
IET SIGNAL PROCESSING | 2018年 / 12卷 / 05期
基金：
中国国家自然科学基金;
关键词：
quantisation (signal); message passing; learning (artificial intelligence); compressed sensing; expectation-maximisation algorithm; iterative methods; adaptive one-bit quantisation; approximate message passing; nearest neighbour sparsity pattern learning; structured sparse signal recovery; a priori distribution; generalised approximate message passing; expectation maximisation method; iterative estimation; GAMP-EM-AD-NNSPL method; SENSING MATRIX PERTURBATION; PARAMETER-ESTIMATION; SIGN MEASUREMENTS; RECOVERY; ALGORITHM;
D O I：
10.1049/iet-spr.2017.0568
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 study, the problem of recovering structured sparse signals with a priori distribution whose structure patterns are unknown is studied from one-bit adaptive (AD) quantised measurements. A generalised approximate message passing (GAMP) algorithm is utilised, and an expectation maximisation (EM) method is embedded in the algorithm to iteratively estimate the unknown a priori distribution. In addition, the nearest neighbour sparsity pattern learning (NNSPL) method is adopted to further improve the recovery performance of the structured sparse signals. Numerical results demonstrate the effectiveness of GAMP-EM-AD-NNSPL method with both simulated and real data.
引用
收藏
页码：629 / 635
页数：7
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