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.
Reconstruction of MMV problem based on OMMP algorithm
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.
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