Cross entropy approximation of structured Gaussian covariance matrices

被引:7
作者
Liou, Cheng-Yuan [1 ]
Musicus, Bruce R. [1 ]
机构
[1] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 10764, Taiwan
关键词
array beamforming; eigenvector methods; factor analysis; generalized principle component analysis; Kullback information measure; minimum cross entropy (CE); oblique transformation; stochastic estimation; structured covariance;
D O I
10.1109/TSP.2008.917878
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 and of the oblique transformation in factor analysis. Furthermore, the corresponding cross entropies (CE) are related to the MDL model order selection criterion.
引用
收藏
页码:3362 / 3367
页数:6
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