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Proceedings Paper

YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing
Author(s): Muhammad A. Lodhi; Sergey Voronin; Waheed U. Bajwa
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Paper Abstract

State-of-the-art sparse recovery methods often rely on the restricted isometry property for their theoretical guarantees. However, they cannot explicitly incorporate metrics such as restricted isometry constants within their recovery procedures due to the computational intractability of calculating such metrics. This paper formulates an iterative algorithm, termed yet another matching pursuit algorithm (YAMPA), for recovery of sparse signals from compressive measurements. YAMPA differs from other pursuit algorithms in that: (i) it adapts to the measurement matrix using a threshold that is explicitly dependent on two computable coherence metrics of the matrix, and (ii) it does not require knowledge of the signal sparsity. Performance comparisons of YAMPA against other matching pursuit and approximate message passing algorithms are made for several types of measurement matrices. These results show that while state-of-the-art approximate message passing algorithms outperform other algorithms (including YAMPA) in the case of well-conditioned random matrices, they completely break down in the case of ill-conditioned measurement matrices. On the other hand, YAMPA and comparable pursuit algorithms not only result in reasonable performance for well-conditioned matrices, but their performance also degrades gracefully for ill-conditioned matrices. The paper also shows that YAMPA uniformly outperforms other pursuit algorithms for the case of thresholding parameters chosen in a clairvoyant fashion. Further, when combined with a simple and fast technique for selecting thresholding parameters in the case of ill-conditioned matrices, YAMPA outperforms other pursuit algorithms in the regime of low undersampling, although some of these algorithms can outperform YAMPA in the regime of high undersampling in this setting.

Paper Details

Date Published: 4 May 2016
PDF: 18 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570E (4 May 2016); doi: 10.1117/12.2224334
Show Author Affiliations
Muhammad A. Lodhi, Rutgers, The State Univ. of New Jersey (United States)
Sergey Voronin, Univ. of Colorado Boulder (United States)
Waheed U. Bajwa, Rutgers, The State Univ. of New Jersey (United States)

Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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