Cross Entropy Approximation of Structured Covariance Matrices

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dc.creatorLiou, Cheng-Yuan
dc.creatorMusicus, Bruce R.
dc.date2006-08-30
dc.date.accessioned2026-07-07T09:46:14Z
dc.date.available2026-07-07T09:46:14Z
dc.descriptionWe apply two variations of the principle of Minimum Cross Entropy (the Kullback information measure) to fit parameterized probability density models to observed data densities. For an array beamforming problem with P incident narrowband point sources, N > P sensors, and colored noise, both approaches yield eigenvector fitting methods similar to that of the MUSIC algorithm[1]. Furthermore, the corresponding cross-entropies are related to the MDL model order selection criterion[2].
dc.description20 pages
dc.identifierhttps://arxiv.org/abs/cs/0608121
dc.identifierhttp://arxiv.org/abs/cs/0608121
dc.identifierIEEE Transactions on Signal Processing, vol. 56, issue 7, Part 2, pages 3362-3367, 2008
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/163460
dc.subjectInformation Theory
dc.titleCross Entropy Approximation of Structured Covariance Matrices
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