4 research outputs found
Generalized Orthogonal Matching Pursuit
As a greedy algorithm to recover sparse signals from compressed measurements,
orthogonal matching pursuit (OMP) algorithm has received much attention in
recent years. In this paper, we introduce an extension of the OMP for pursuing
efficiency in reconstructing sparse signals. Our approach, henceforth referred
to as generalized OMP (gOMP), is literally a generalization of the OMP in the
sense that multiple indices are identified per iteration. Owing to the
selection of multiple ''correct'' indices, the gOMP algorithm is finished with
much smaller number of iterations when compared to the OMP. We show that the
gOMP can perfectly reconstruct any -sparse signals (), provided that
the sensing matrix satisfies the RIP with . We also demonstrate by empirical
simulations that the gOMP has excellent recovery performance comparable to
-minimization technique with fast processing speed and competitive
computational complexity