Fusion estimation for two sensors with nonuniform estimation rates

The fusion estimation is investigated in this paper for two-sensor discrete-time stochastic systems. A finite-horizon optimal linear estimator is designed for each sensor to generate local estimates with a nonuniform estimation rate. Then, a fusion rule with matrix weights in the linear minimum variance sense is designed for each sensor to fuse local estimates from itself and the other sensors. The proposed algorithm reduces to the one that can be used to design asynchronous fusion estimators with uncorrelated measurement noises. Finally, the effectiveness of the proposed results is illustrated by a simulation example of a maneuvering target tracking system.