Signal reconstruction in sensor arrays using sparse representations


We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse representation in a chosen dictionary in time domain. This leads to a large scale convex optimization problem, which involves combined l1-l2 norm minimization. The optimization is carried by the truncated Newton method, using preconditioned conjugate gradients in inner iterations. The byproduct of reconstruction is the estimation of source locations. r 2005 Published by Elsevier B.V.

DOI: 10.1016/j.sigpro.2005.05.033

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@article{Model2006SignalRI, title={Signal reconstruction in sensor arrays using sparse representations}, author={Dmitri Model and Michael Zibulevsky}, journal={Signal Processing}, year={2006}, volume={86}, pages={624-638} }