Oblique Projection Matching Pursuit

Jian Wang, Feng Wang, Yunquan Dong, Byonghyo Shim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is preserved in the residual vector. Further, to improve the information preservation, we present a modification of OMP, called oblique projection matching pursuit (ObMP), which updates the residual in a oblique projection manor. Using the restricted isometric property (RIP), we build a solid yet very intuitive grasp of the more accurate phenomenon of ObMP. We also show from empirical experiments that the ObMP achieves improved reconstruction performance over the conventional OMP algorithm in terms of support detection ratio and mean squared error (MSE).

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalMobile Networks and Applications
Publication statusAccepted/In press - 2016 Nov 22
Externally publishedYes


  • Compressed sensing (CS)
  • Orthogonal matching pursuit (OMP)
  • Restricted isometry property (RIP)
  • Sparse recovery

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications


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