基于子空间方法的联合稀疏恢复，通过MUSIC算法进行结合测试，给出了测试结果-We propose subspace-augmented

MUSIC (SA-MUSIC), which improves on MUSIC such that the

support is reliably recovered under such unfavorable conditions.

Combined with a subspace-based greedy algorithm, known as Orthogonal

Subspace Matching Pursuit, which is also proposed and

analyzed in this paper, SA-MUSIC provides a computationally

efficient algorithm with a performance guarantee. The performance

guarantees are given in terms of a version of the restricted

isometry property. In particular, we also present a non-asymptotic

perturbation analysis of the signal subspace estimation step, which

has been missing in the previous studies of MUSIC.