多测量向量模型下的修正MUSIC算法

压缩感知多测量向量(MMV)模型用于解决具有相同稀疏结构的多快拍问题，在传统阵列信号处理应用中多重信号分类(MUSIC)方法是一种常见的方法，但当快拍数不足(低于稀疏度)时其性能将急剧恶化。Kim等人(2012)推导出一种修正的MUSIC谱，并将压缩重构方法和MUSIC算法结合提出压缩感知MUSIC算法(CS-MUSIC)，能够有效克服快拍数不足的问题。该文将Kim等人的结论扩展到一般情形，并基于传统的MUSIC谱和CS-MUSIC谱提出一种修正的MUSIC算法(MMUSIC)。仿真结果表明所提算法能够有效克服快拍数不足的问题，并且具有比CS-MUSIC算法和压缩感知贪婪算法更高的重构概率。

Modified MUSIC Algorithm for Multiple Measurement Vector Models

The Compressed Sensing (CS) Multiple Measurement Vector (MMV) model is used to solve multiple snapshots problem with the same sparse structure. MUltiple SIgnal Classification (MUSIC) is a common method in traditional array signal processing applications. However, when the number of snapshots is below sparsity performance will be dramatically deteriorated. Kim et al. derive a modified MUSIC spectral method and propose a Compressed Sensing MUSIC method (CS-MUSIC) combining the compression reconstruction method and the MUSIC algorithm, which can effectively overcome the problem of insufficient snapshot number. In this paper, Kim et al.’s conclusion is extended to the general case, and a Modified MUSIC (MMUSIC) algorithm is proposed based on the traditional MUSIC method and the CS-MUSIC method. The simulation results show that the proposed algorithm can effectively overcome the shortage of snapshots and has a higher reconstruction probability than the CS-MUSIC algorithm and the compressed sensing greedy algorithm.