Improved Sufficient Conditions for Sparse Recovery with Generalized Orthogonal Matching Pursuit

Generalized orthogonal matching pursuit (gOMP), also called orthogonal multi-matching pursuit, is an extension of OMP in the sense that N ≥ 1 indices are identified per iteration. In this paper, we show that if the restricted isometry constant (RIC) δNK+1 of a sensing matrix A satisfies δNK+1 < 1/ √ K/N + 1, then under a condition on the signal-to-noise ratio, gOMP identifies at least one index in the support of any K-sparse signal x from y = Ax+ v at each iteration, where v is a noise vector. Surprisingly, this condition does not require N ≤ K which is needed in Wang, et al 2012 and Liu, et al 2012. Thus, N can have more choices. When N = 1, it reduces to be a sufficient condition for OMP, which is less restrictive than that proposed in Wang 2015. Moreover, in the noise-free case, it is a sufficient condition for accurately recovering x in K iterations which is less restrictive than the best known one. In particular, it reduces to the sharp condition proposed in Mo 2015 when N = 1.