基于冗余字典的联合稀疏同步迭代硬阈值算法  

Simultaneous iterative hard thresholding for joint sparse recovery based on redundant dictionaries

作　　者：陈鹏[1] 孟晨[1] 王成[1] 陈华[1] 

机构地区：[1]军械工程学院导弹工程系,石家庄050003

出　　处：《计算机应用》2015年第9期2508-2512,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61372039)

摘　　要：为了改进基于压缩感知(CS)的欠Nyquist采样系统在冗余字典条件下信号重构的效果,研究了基于ε-闭包的分块联合稀疏模型的同步迭代硬阈值(SIHT)算法。分析了采样系统基于多测量向量(MMV)的CS合成模型,提出了ε-闭包的分块相干性和约束等距特性(RIP)概念;在迭代过程中根据冗余字典分块相干性,对更新支撑集进行优选从而完成算法改进;给出了迭代收敛常数,并分析了改进型算法的收敛特性。仿真实验结果表明,相比传统算法,改进型算法在采样系统足够的通道数条件下重构成功率可达到100%,噪声抑制能力能够提高7 d B^9 d B,总运算时间可以降低至少37.9%,信号重构收敛速度更快。For improving recovery performance of signals sampled by sub-Nyquist sampling system with Compressed Sensing （CS）, the block Simultaneous Iterative Hard Thresholding （SIHT） recovery algorithm for joint sparse model based on s-closure was proposed. Firstly, The CS synthesis model for Muhiple Measurement Vector （MMV） of sampling system was analyzed and the concepts of e-coherence and Restricted Isometry Property （RIP） were proposed. Then, according to the block coherence of redundant dictionaries, the SIHT algorithm was improved by optimizing the support sets in iterations. In addition, the iterative convergence constant was given and the algorithm convergence property was analyzed. At last, the simulation experiments show that, compared with traditional method, the new algorithm can achieve recovery success rate of 100% with enough sampling channels, while the noise suppressing ability was increased by 7 dB to 9 dB and the total execution time was brought down by at least 37.9%, with higher convergence speed.

关 键 词：冗余字典 联合稀疏 迭代硬阈值 相干性 欠Nyquist采样 

分 类 号：TN911.72[电子电信—通信与信息系统]