Distributed fusion filter for asynchronous multi-rate multi-sensor non-uniform sampling systems

This paper is concerned with the distributed fusion filtering problem for a class of asynchronous multi-rate multisensor non-uniform sampling discrete stochastic systems, where the state is updated at the highest sampling rate and different sensors may have different lower measurement sampling rates. Furthermore, the state is updated uniformly and the measurement is sampled non-uniformly. The non-augmented state models at each sensor are established by considering the system noises. The local filters at measurement sampling points of each sensor are designed based on the established state space models by an innovation analysis approach. Further, the local filters at state update points are proposed. The corresponding filtering error covariance matrices are derived. Using the covariance intersection fusion algorithm, the distributed fusion filter is given based on the local filters and the local filtering error covariance matrices. The proposed algorithm can significantly improve the estimation accuracy compared to the previous modeling method which ignores the system noise. The simulation research verifies the effectiveness of the proposed algorithm.