Reducing basis mismatch in harmonic signal recovery via alternating convex search

Published

Journal Article

The theory behind compressive sampling pre-supposes that a given sequence of observations may be exactly represented by a linear combination of a small number of basis vectors. In practice, however, even small deviations from an exact signal model can result in dramatic increases in estimation error; this is the so-called 'basis mismatch' problem. This work provides one possible solution to this problem in the form of an iterative, biconvex search algorithm. The approach uses standard ℓ1-minimization to find the signal model coefficients followed by a maximum likelihood estimate of the signal model. The algorithm is illustrated on harmonic signals of varying sparsity and outperforms the current state-of-the-art. © 1994-2012 IEEE.

Full Text

Cited Authors

  • Nichols, JM; Oh, AK; Willett, RM

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 21 / 8

Start / End Page

  • 1007 - 1011

International Standard Serial Number (ISSN)

  • 1070-9908

Digital Object Identifier (DOI)

  • 10.1109/LSP.2014.2322444

Citation Source

  • Scopus