Accession Number:

ADA528509

Title:

Sparsity Adaptive Matching Pursuit Algorithm for Practical Compressed Sensing

Descriptive Note:

Research rept.

Corporate Author:

JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Report Date:

2008-01-01

Pagination or Media Count:

8.0

Abstract:

This paper presents a novel iterative greedy reconstruction algorithm for practical compressed sensing CS, called the sparsity adaptive matching pursuit SAMP. Compared with other state-of-the-art greedy algorithms, the most innovative feature of the SAMP is its capability of signal reconstruction without prior information of the sparsity. This makes it a promising candidate for many practical applications when the number of non-zero significant coefficients of a signal is not available. The proposed algorithm adopts a similar flavor of the EM algorithm, which alternatively estimates the sparsity and the true support set of the target signals. In fact, SAMP provides a generalized greedy reconstruction framework in which the orthogonal matching pursuit and the subspace pursuit can be viewed as its special cases. Such a connection also gives us an intuitive justification of trade-offs between computational complexity and reconstruction performance. While the SAMP offers a comparably theoretical guarantees as the best optimization-based approach simulation results show that it outperforms many existing iterative algorithms, especially for compressible signals.

Subject Categories:

  • Numerical Mathematics
  • Computer Programming and Software
  • Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE