基于OMMP算法的多测量向量问题的重构
Reconstruction of MMV problem based on OMMP algorithm
冯晓艳 1王金平1
作者信息
- 1. 宁波大学数学与统计学院,浙江宁波 315211
- 折叠
摘要
正交多匹配追踪算法(OMMP算法)是正交匹配追踪算法(OMP算法)的一种拓展,近年来受到很多相关研究人员的关注.不同于OMP算法,OMMP算法在每次迭代中识别多个指标.本文分析了在限制等距性(RIP)和多向量信噪比(MSNR)条件下,用于解决多测量向量问题的OMMP算法的鲁棒性.此外,在给出的限制等距常数(RIC)的条件下,用归纳假设的方法证明了当V=0以及整数N满足1 ≤ N ≤(m-1)/K时,OMMP算法可以准确恢复K-行稀疏矩阵X.
Abstract
As an extension of OMP,orthogonal multi-matching pursuit(OMMP)has received much attention in recent years.Unlike the OMP,OMMP algorithm identifies N≥1 indexes per iteration.In this paper,the robustness of the OMMP for multiple measurement vector(MMV)problem under the restricted isometry property(RIP)and multi-signal-to-noise ratio(MSNR),which will be mentioned in introduction,is presented.Furthermore,the induction hypothesis is used to show that the OMMP algorithm can exactly recover K-row sparse matrix in K iterations under the RIP condition presented when V=0 and integer N with 1≤N≤(m-1)/K.
关键词
压缩感知/正交多匹配追踪/限制等距性/多测量向量Key words
compressed sensing/orthogonal multi-matching pursuit/restricted isometry property/multiple measurement vector引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据