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Proceedings Paper

Fixing basis mismatch in compressively sampled photonic link
Author(s): J. M. Nichols; F. Bucholtz; C. V. McLaughlin; A. K. Oh; R. M. Willett
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Paper Abstract

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 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 process is repeated until a convergence criteria is met. The algorithm is illustrated on harmonic signals of varying sparsity and outperforms the current state-of-the-art.

Paper Details

Date Published: 22 May 2014
PDF: 5 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180N (22 May 2014); doi: 10.1117/12.2053739
Show Author Affiliations
J. M. Nichols, U.S. Naval Research Lab. (United States)
F. Bucholtz, U.S. Naval Research Lab. (United States)
C. V. McLaughlin, U.S. Naval Research Lab. (United States)
A. K. Oh, Univ. of Wisconsin-Madison (United States)
R. M. Willett, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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