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Wideband source localization using sparse learning via iterative minimization
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2013 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 93, no 12 SI, 3504-3514 p.Article in journal (Refereed) Published
Abstract [en]

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 sourcelocalization performance, and are able to resolve the left-right ambiguity problem, when used together with the vector sensor array technology. 

Place, publisher, year, edition, pages
2013. Vol. 93, no 12 SI, 3504-3514 p.
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-205724DOI: 10.1016/j.sigpro.2013.04.005ISI: 000324354900024OAI: oai:DiVA.org:uu-205724DiVA: diva2:642563
Available from: 2013-08-22 Created: 2013-08-22 Last updated: 2017-12-06Bibliographically approved

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Stoica, Peter

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