A modified multiple OLS (m2OLS) algorithm for signal recovery in compressive sensing

Abstract

Orthogonal least square (OLS) is an important sparse signal recovery algorithm in compressive sensing, which enjoys superior probability of success over other well known recovery algorithms under conditions of correlated measurement matrices. Multiple OLS (mOLS) is a recently proposed improved version of OLS which selects multiple candidates per iteration by generalizing the greedy selection principle used in OLS and enjoys faster convergence than OLS. In this paper, we present a refined version of the mOLS algorithm where at each step of iteration, we first preselect a submatrix of the measurement matrix suitably and then apply the mOLS computations to the chosen submatrix. Since mOLS now works only on a submatrix and not on the overall matrix, computations reduce drastically. Convergence of the algorithm, however, requires to ensure passage of true candidates through the two stages of preselection and mOLS based selection successively. This paper presents convergence conditions for both noisy and noise free signal models. The proposed algorithm enjoys faster convergence properties similar to mOLS, at a much reduced computational complexity.

Required Number of Iterations for Sparse Signal Recovery via Orthogonal Least Squares

“…Wen et al [19] and Geng et al [20] utilized the restricted isometry property (RIP), which is defined as follows, to study the sufficient condition of exact recovery of x with OLS. Using the RIP, the authors in [21][22][23][24] discussed the performance of multiple OLS which is an extension of OLS.…”

Analysis of the modified multiple OLS (mbm$^2$OLS) algorithm for sparse signal recovery with noise

“…如 A*OMP [16] 、广义协 方差匹配追踪 (generalized covariance-assisted matching pursuit) [17] 和广义正交匹配追踪 (generalized OMP, gOMP) [18][19][20][21] 等. 最近, 在 mOLS 算法基础上, Mukhopadhyay 等 [22] [22] 算法 输入:…”