Joint sparse recovery condition via orthogonal matching pursuit

Rui Qi; Xiangkun Ji; Haojie Yuan

doi:10.1117/12.3017048

15 January 2024 Joint sparse recovery condition via orthogonal matching pursuit

Rui Qi, Xiangkun Ji, Haojie Yuan

Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 129830L (2024) https://doi.org/10.1117/12.3017048
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China

Abstract

In order to solve multiple measurement vector (MMV) sparse reconstruction problem which recovering the K row sparse matrix X by using Y=AX, the article studies the accurate reconstruction condition of orthogonal matching algorithm (OMP) with respect to (MMV) sparse reconstruction problem. Restricted isometry property (RIP) condition is used to evaluate whether the proposed algorithm can be reconstructed accurately. The article proposes a sufficient condition based on RIP. There has been proven that the OMP algorithm can be used to ensure an accurate recovery process when the K+1th order RIP constant of matrix A satisfying δ_K+1<1/√K+1. In particular, for a K-row sparse matrix X, when the K+1th order RIP constant of the coefficient matrix A is equal to 1/√k+1, the OMP algorithm may fail. In other words, the terms of the proposal are very strict.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Qi, Xiangkun Ji, and Haojie Yuan "Joint sparse recovery condition via orthogonal matching pursuit", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 129830L (15 January 2024); https://doi.org/10.1117/12.3017048

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