This paper is to study the linear minimum variance estimation for discretetime systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous a...This paper is to study the linear minimum variance estimation for discretetime systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous and double time-delayed measurements. It is shown that the derived estimator involves solving three different standard Kalman filtering with the same dimension as the original system. The obtained results form the basis for solving some complicated problems such as H, fixed-lag smoothing, preview control, H, filtering and control with time delays.展开更多
基金the National Natural Science Foundation of China （Grant Nos. 60574016, 60474058, 60534010）.
文摘This paper is to study the linear minimum variance estimation for discretetime systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous and double time-delayed measurements. It is shown that the derived estimator involves solving three different standard Kalman filtering with the same dimension as the original system. The obtained results form the basis for solving some complicated problems such as H, fixed-lag smoothing, preview control, H, filtering and control with time delays.
