结构协方差矩阵的交叉熵逼近
Cross Entropy Approximation of Structured Covariance Matrices
摘要：
我们应用最小交叉熵（Kullback信息测度）原理的两种变化来拟合参数化的概率密度模型以观测到的数据密度。对于具有P个入射窄带点源、N> P个传感器和有色噪声的阵列波束形成问题，这两种方法都产生与MUSIC算法类似的特征向量拟合方法[1]。此外，相应的交叉熵与MDL模型阶数选择准则有关[2]。
英文摘要：
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[1]. Furthermore, the corresponding cross-entropies are related to the MDL model order selection criterion[2].
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