Title: Evolutionary algorithm for compressive sensing

Authors: Uday K. Chakraborty

Addresses: Department of Mathematics and Computer Science, One University Blvd., University of Missouri, St. Louis, MO 63121, USA

Abstract: This paper presents a non-traditional approach to compressive sensing, by developing an evolutionary algorithm-based method for signal reconstruction. Previous work in signal reconstruction in compressive sensing focused primarily on framing the problem as a convex optimisation that minimises the 1 norm of the signal. Minimising the 2 norm does not help as it leads to a non-sparse solution. Minimising the 0 norm is known to be NP-complete, thereby requiring exhaustive enumeration which is computationally prohibitive. Our approach is different from the methods adopted in the literature and is capable of handling 0-, 1- or 2-norm minimisation, and linear or nonlinear combinations thereof, in the same framework, yielding fairly good signal recovery with high probability. We provide empirical results on a number of test problems.

Keywords: compressive sensing; compressive sampling; signal recovery; evolutionary algorithm; sparsity; signal reconstruction.

DOI: 10.1504/IJAAC.2015.068053

International Journal of Automation and Control, 2015 Vol.9 No.1, pp.61 - 70

Received: 05 May 2014
Accepted: 30 May 2014

Published online: 15 Mar 2015 *

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