Analysis and extension of existing bounds in compressed sensing

This paper presents analysis and extension about known bounds in compressed basing on the Orthogonal Matching Pursuit algorithm. In the noiseless case, we focus on factors such as Restricted Isometry Property (RIP), Mutual Incoherence Property (MIP) and the recovery order of entries. For the noisy compressed sensing, we have proved two corollaries, exploiting the idea of noise folding, and obtained an upper bound of Signal-to-Noise Ratios (SNR) for the exact recovery.