联合稀疏多测量向量恢复的MMV子空间追踪算法

2019 IEEE 13th International Conference on ASIC (ASICON) Pub Date : 2019-10-01 DOI:10.1109/ASICON47005.2019.8983646

Sujuan Liu, Lili Zheng, Lei Liu, Qianjin Lin

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引用次数: 0

摘要

针对联合稀疏多测量向量(MMV)问题，提出了MMV子空间追踪(M-SP)算法。由于M-SP采用了预选和回溯机制，因此M-SP不仅具有比现有算法更高的恢复性能，而且显著减少了迭代次数，提高了信号恢复效率。仿真结果表明，M-SP与同步压缩采样匹配追踪(SCoSaMP)具有几乎相同的恢复性能和迭代次数，但M-SP显著降低了每次迭代的计算复杂度。例如，当稀疏度$K$为5时，每次迭代M-SP的计算复杂度为SCoSaMP的24.0%。

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MMV Subspace Pursuit (M-SP) Algorithm for Joint Sparse Multiple Measurement Vectors Recovery

In this paper, MMV Subspace Pursuit (M-SP) algorithm is proposed for solving joint sparse multiple measurement vectors (MMV) problem. The pre-selection and backtracking mechanisms are used in M-SP, so M-SP not only has higher recovery performance than some existing algorithms, but also significantly reduces the iteration number for improving the signal recovery efficiency. Simulations results show that M-SP and Simultaneous Compressive Sampling Matching Pursuit (SCoSaMP) have almost identical recovery performance and iteration times, but M-SP significantly reduces the computation complexity in per iteration. For example, when sparsity $K$ is 5, the computational complexity of M-SP is 24.0% of that of SCoSaMP in each iteration.

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来源期刊

2019 IEEE 13th International Conference on ASIC (ASICON)

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