Splitting Matching Pursuit Method for Reconstructing Sparse Signal in Compressed Sensing

[摘要] In this paper, a novel method named as splitting matching pursuit (SMP) is proposed to reconstructK-sparsesignal in compressed sensing. The proposed method selectsFl  (Fl>2K)largest components of the correlationvectorc, which are divided intoFsplit sets with equal lengthl. The searching area is thus expanded to incorporatemore candidate components, which increases the probability of finding the true components at one iteration. Theproposed method does not require the sparsity levelKto be known in prior. The Merging, Estimation and Pruningsteps are carried out for each split set independently, which makes it especially suitable for parallel computation. Theproposed SMP method is then extended to more practical condition, e.g. the direction of arrival (DOA) estimationproblem in phased array radar system using compressed sensing. Numerical simulations show that the proposedmethod succeeds in identifying multiple targets in a sparse radar scene, outperforming other OMP-type methods. The proposed method also obtains more precise estimation of DOA angle using one snapshot compared with thetraditional estimation methods such as Capon, APES (amplitude and phase estimation) and GLRT (generalizedlikelihood ratio test) based on hundreds of snapshots.

[学科分类] 应用数学