Journal of Applied Mathematics
Volume 2013 (2013), Article ID 804640, 8 pages
Research Article

Splitting Matching Pursuit Method for Reconstructing Sparse Signal in Compressed Sensing

1The School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
2The MOE Key Lab for Intelligent and Networked Systems, The School of Electronic and Information Engineering, Xian Jiaotong University, Xi’an, Shaanxi 710049, China

Received 14 March 2013; Accepted 16 April 2013

Academic Editor: Xianxia Zhang

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.