Analyzing of the fusion estimation algorithm of a class of dynamic multiscale system

The fusion estimation algorithm of a class of dynamic multiscale system based on state space projection is explained for clearly understanding of its essence in this paper. Firstly, the fusion estimation algorithm is compared with that directly performing Kalman filter at the finest scale. Simulation results show that performance of the fusion estimation algorithm outperforms that of directly performing Kalman filter at the finest scale. Secondly, the fusion estimation algorithm is compared with general time registration method in terms of algorithm process and computational cost. It is shown that, time registration is replaced with rigorous mathematical model in the fusion estimation algorithm, and optimal estimation based on measurement information at all scales can be obtained, and at the same time, the computational cost is larger than that of time registration method. It thus lays a foundation for practical application of the fusion estimation algorithm for a class of dynamic multiscale system.