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Wideband source localization using sparse learning via iterative minimization
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DOI：10.1016/j.sigpro.2013.04.005.png)
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Abstract
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In this paper, two extensions of the Sparse Learning via Iterative Minimization (SLIM) algorithm are presented for wideband source localization using a sensor array. The proposed methods exploit the joint sparse structure across all frequency bins, and estimate the spatial pseudo-spectra at various frequency bins jointly and iteratively. Via several numerical examples, we show that the proposed methods can provide high-resolution angle estimates and excellent source localization performance, and are able to resolve the left-right ambiguity problem, when used together with the vector sensor array technology. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Wideband source localization
Sparse signal recovery
Vector sensor array
Sparse learning via iterative minimization (SLIM)
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