A signal recovery algorithm on multiple measurement vectors of arbitrary sparse structure with impulsive noise

被引：0

作者：

Peng J. ^{[1
,2
]}

Han Z. ^{[2
]}

You X. ^{[1
,2
]}

Yang P. ^{[2
]}

机构：

[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] Wuhan Maritime Communication Research Institute, Wuhan

来源：

Peng, Junwei (pengjunwei@hrbeu.edu.cn) | 1806年 / Editorial Board of Journal of Harbin Engineering卷 / 38期

关键词：

Compressed sensing; Conjugate gradient method; Impulsive noise; Multiple measurement vectors; Objective functions; Signal recovery;

D O I：

10.11990/jheu.201610074

中图分类号：

学科分类号：

摘要：

A novel sparse signal recovery algorithm on multiple measurement vectors of arbitrary sparse structure (ASS-MMV) with impulsive noise was proposed to deal with the robustness and universality issues within most existing sparse signal recovery algorithms. In the beginning, the objective function for sparse optimization was built based on smoothed L0-norm constrained Lorentzian norm regularization, and the ASS-MMV recovery model was set up in the presence of impulsive noise. After that, a parallel recovery which speeds up the convergence and improves the operating efficiency was implemented in a unified parametric framework by combining the fixed-step formula and the conjugate gradient algorithm with sufficient decent property. Simulation results demonstrate that the proposed algorithm can effectively recover the MMV signal with arbitrary sparse structure in impulsive noise environment. It is also proved that the proposed algorithm has faster recovery speed and less computing cost, but better robustness against the noise. © 2017, Editorial Department of Journal of HEU. All right reserved.

引用

页码：1806 / 1811

页数：5

共 15 条

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