Cross Entropy Approximation of Structured Covariance Matrices
Information Theory
2008-06-24 v1 math.IT
Abstract
We 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].
Cite
@article{arxiv.cs/0608121,
title = {Cross Entropy Approximation of Structured Covariance Matrices},
author = {Cheng-Yuan Liou and Bruce R. Musicus},
journal= {arXiv preprint arXiv:cs/0608121},
year = {2008}
}
Comments
20 pages