Study of asynchronous multirate data fusion estimation algorithm based on nonlinear systems

A kind of time-vary nonlinear dynamic system is studied in this paper. An effective data fusion state estimation algorithm is presented in time of multiple sensors observing a single target with different sampling rates asynchronously. The asynchronous multirate system is transformed to synchronous multirate system by use of the established multiscale models. In each step, to get the state estimate, state prediction is followed by state update. In state and measurements prediction step, strong tracking filter (STF) is used. While, in state update step, distributed structure with feedback is used, and the fused state estimate is obtained by sequentially use of the measurements observed by different sensors. The augmentation of state or measurement dimensions are avoided by use of the presented method, and the real-time property of the algorithm is guaranteed. Simulation results show the effectiveness of the proposed algorithm.