Entropy-Based Covariance Determinant Estimation

被引：0

作者：

De Cabrera, Ferran ^{[1
]}

Riba, Jaume ^{[1
]}

Vazquez, Gregori ^{[1
]}

机构：

[1] Tech Univ Catalonia UPC, Signal Theory & Commun Dept, Barcelona, Spain

来源：

2017 IEEE 18TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC) | 2017年

关键词：

Renyi entropy; Information Potential; U-Statistics; Hadamard Ratio; Kernel Methods; Cramer-Rao Bound (CRB); Spectrum Sensing; Cognitive Radio; ROBUST ESTIMATION;

D O I：

暂无

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：

An information-theoretic approach is described to estimate the determinant of the covariance matrix of a random vector sequence (a common task in a wide range of estimation and detection problems in signal processing for communications). The method is based on a prior entropy-based processing of the data using kernels and offers robustness against small-entropy contamination. The trade-off between optimality, accuracy and robustness is analyzed, along with the impact of the relative kernel bandwidth and data size.

引用

页数：5

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