Robust Linear Estimation with Second Order Statistics Information Uncertainty
In this paper,we develop a robust linear estimation (RLE)in presence of a priori statistical information with uncer-tainties without a model of a with uncertainty but without assumption of model of parameter under estimation and observation. We assume that a random vector x is observed through a nonlinear (or linear)transformation y=f (x ,w),where w is noise. We consider the case that there are some uncertainties in second order statistical information of x and y ,i.e.,C x ,C yx and C y and propose an optimal minimax linear estimator that minimizes worst case mean-squared error (MSE)in the region of uncer-tainty.The minimax estimator can be formulated as a solution to a semide finite programming problem (SDP).We consider both the Frobenius norm and spectral norm of the uncertainty constraints,leading to the two corresponding robust linear estimators. Finally,Numerical examples are given which illustrates the effectiveness of the proposed estimators.
SONG Enbin ZHU Yunmin ZHOU Jie SHEN Xiaojing
College of Mathematics,Sichuan University,Chengdu 610064,Sichuan,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）