Copyright © 2013 Liu Jing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this paper, a novel method named as splitting matching pursuit (SMP) is proposed to reconstruct -sparse
signal in compressed sensing. The proposed method selects largest components of the correlation
vector , which are divided into split sets with equal length . The searching area is thus expanded to incorporate
more candidate components, which increases the probability of finding the true components at one iteration. The
proposed method does not require the sparsity level to be known in prior. The Merging, Estimation and Pruning
steps are carried out for each split set independently, which makes it especially suitable for parallel computation. The
proposed SMP method is then extended to more practical condition, e.g. the direction of arrival (DOA) estimation
problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed
method 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 the
traditional estimation methods such as Capon, APES (amplitude and phase estimation) and GLRT (generalized
likelihood ratio test) based on hundreds of snapshots.