冲击噪声下任意稀疏结构的MMV重构算法  

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

作　　者：彭军伟[1,2] 韩志韧 游行远[1,2] 杨平 PENG Junwei;HAN Zhiren;YOU Xingyuan;YANG Ping(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;Wuhan Maritime Communication Research Institute, Wuhan 430079, China)

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001 [2]武汉船舶通信研究所,湖北武汉430079

出　　处：《哈尔滨工程大学学报》2017年第11期1806-1811,共6页Journal of Harbin Engineering University

基　　金：船舶工业国防科技预研基金项目(11J3.4.2)

摘　　要：针对现有多测量向量(multiple measurement vectors,MMV)模型稀疏重构算法在冲击噪声背景下存在的鲁棒性不强、适用性不广等问题,本文提出了一种冲击噪声下任意稀疏结构的MMV模型(ASS-MMV)稀疏重构算法。该算法利用Lorentzian范数和矩阵平滑零范数正则化构造稀疏优化目标函数,建立冲击噪声背景下ASS-MMV重构模型;结合固定步长公式和具有充分下降性质的共轭梯度算法,在统一参数框架下并行重构,以提高算法收敛速度和运行效率。仿真结果表明:本文算法能够在冲击噪声背景下高质量的重构任意稀疏结构的MMV信号,对噪声具有一定的鲁棒性,并且收敛速度较快、计算开销更小。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 L0norm 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.

关 键 词：压缩感知 稀疏重构 多量测向量 目标函数 冲击噪声 共轭梯度方法 

分 类 号：TN911[电子电信—通信与信息系统]