화학공학소재연구정보센터
IEEE Transactions on Automatic Control, Vol.51, No.4, 686-689, 2006
Optimal linear estimation and data fusion
Optimal mean square linear estimators are determined for general uncorrelated noise. We allow the noise variance matrix in the observation process to be singular. This requires properties of generalized inverses which are developed in Section It. The proofs appear to be new. When there are two observation sequences the optimal method of recursively fusing the two is determined. We derive a new formula for the covariance of the two estimates which then provides exact dynamics for a fused estimate.