Signal reconstruction in sensor arrays using temporal-spatial sparsity regularization

Abstract

We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse [wavelet-type] representation in time domain. This leads to a large scale convex optimization problem, which involves l1 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. © Springer-Verlag 2004.