Covariance Matrix Selection in Covariance Shaping Least Square Estimation  

作　　者：DU Huiqian MEI Wenbo SU Guangchuan 

机构地区：[1]Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China

出　　处：《Chinese Journal of Electronics》2007年第2期295-298,共4页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.60472021）.

摘　　要：The Covariance shaping least square （CSLS） estimator can obtain lower Mean square error （MSE） than Least square （LS） estimator at moderate to low Signal-to-noise ratio （SNR）. The crux of CSLS is how to determine the error covariance matrix. In this paper, an algorithm is proposed to obtain the covariance matrix coefficient in white noise observation. The presented estimator restricts the bias to a certain range and keeps smaller variance than the CSLS. It also reaches the Cramer-Rao lower bound for biased estimator. As shown through both theory deduction and simulations, this method improves the performance of the CSLS.

关 键 词：Least square estimation Biased estimation Covariance shaping. 

分 类 号：TN911.7[电子电信—通信与信息系统]