Robust Covariance Estimators Based on Information Divergences and Riemannian Manifold

Entropy

Yongqiang Cheng¹  Hongqiang Wang¹  Xiaoqiang Hua¹  Yuliang Qin¹ 

[1] School of Electronic Science, National University of Defence Technology, Changsha 410073, China;

关键词: information divergence;    Riemannian manifold;    covariance estimation;    mean estimator;    heterogeneous clutter;   

DOI  :  10.3390/e20040219

来源: DOAJ

【 摘 要 】

This paper proposes a class of covariance estimators based on information divergences in heterogeneous environments. In particular, the problem of covariance estimation is reformulated on the Riemannian manifold of Hermitian positive-definite (HPD) matrices. The means associated with information divergences are derived and used as the estimators. Without resorting to the complete knowledge of the probability distribution of the sample data, the geometry of the Riemannian manifold of HPD matrices is considered in mean estimators. Moreover, the robustness of mean estimators is analyzed using the influence function. Simulation results indicate the robustness and superiority of an adaptive normalized matched filter with our proposed estimators compared with the existing alternatives.

【 授权许可】

Unknown