A Framework For Compressive Sensing Of Asymmetric Signals Using Normal And Skew-Normal Mixture Prior

Keywords

Approximate Message Passing; Asymmetrical Signal; Compressive Sensing; Mixture Model

Abstract

In this work, we are interested in the compressive sensing of sparse signals whose significant coefficients are distributed asymmetrically with respect to zero. To properly address this problem, we develop a framework utilizing a two-state normal and skew normal mixture density as the prior distribution of the signal. The significant and insignificant coefficients of the signal are represented by skew normal and normal distributions, respectively. A novel approximate message passing-based algorithm is developed to estimate the signal from its compressed measurements. A fast gradient-based estimator is designed to infer the density of each state. Experiment results on simulated data and two real-world tests, i.e., multi-input multi-output (MIMO) communication system and weather sensor network, confirm that our proposed technique is powerful in exploiting asymmetrical feature, and outperforms many sophisticated methods.

Publication Date

12-1-2015

Publication Title

IEEE Transactions on Communications

Volume

63

Issue

12

Number of Pages

5062-5072

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TCOMM.2015.2488651

Socpus ID

84961798394 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84961798394

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