Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.

In this paper we introduce a new algorithm for the estimation of source location parameters from array data given prior distributions on unknown nuisance source signal parameters. The conditional maximum-likelihood (CML) formulation is employed, and ML estimation is obtained by marginalizing over the nuisance parameters. In general, direct solution of this marginalization ML problem is intractable. We introduce an expectation-maximization (EM) algorithm solution, which is applicable to any prior distribution.

Main Author: Perry, Richard.
Other Authors: Buckley, Kevin.
Language: English
Published: 2000
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:178438
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dc_source_str_mv SAM Workshop, August 6, 2002, 351-355.
author Perry, Richard.
author_s Perry, Richard.
spellingShingle Perry, Richard.
Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
author-letter Perry, Richard.
author_sort_str Perry, Richard.
author2 Buckley, Kevin.
author2Str Buckley, Kevin.
dc_title_str Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
title Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
title_short Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
title_full Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
title_fullStr Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
title_full_unstemmed Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
collection_title_sort_str maximum likelihood source localization using the em algorithm to incorporate prior parameter distributions.
title_sort maximum likelihood source localization using the em algorithm to incorporate prior parameter distributions.
description In this paper we introduce a new algorithm for the estimation of source location parameters from array data given prior distributions on unknown nuisance source signal parameters. The conditional maximum-likelihood (CML) formulation is employed, and ML estimation is obtained by marginalizing over the nuisance parameters. In general, direct solution of this marginalization ML problem is intractable. We introduce an expectation-maximization (EM) algorithm solution, which is applicable to any prior distribution.
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dc.title Maximum likelihood source localization using the EM algorithm to incorporate prior parameter distributions.
dc.creator Perry, Richard.
Buckley, Kevin.
dc.description In this paper we introduce a new algorithm for the estimation of source location parameters from array data given prior distributions on unknown nuisance source signal parameters. The conditional maximum-likelihood (CML) formulation is employed, and ML estimation is obtained by marginalizing over the nuisance parameters. In general, direct solution of this marginalization ML problem is intractable. We introduce an expectation-maximization (EM) algorithm solution, which is applicable to any prior distribution.
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dc.source SAM Workshop, August 6, 2002, 351-355.
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