Sequential fusion for asynchronous multi-sensor system based on Kalman filter

This paper proposes a novel sequential asynchronous fusion algorithm by using the idea of sequential discretization of the sampling points based on a continuous and distributed multi-sensor linear dynamic system. Firstly, it maps and unifies all measurements in the reference frame and clock with fusion centre. Secondly, selecting every sampling time in the fusion period to discretize the continuous state system sequentially, we get the state equation and the relevant measurement equation between every sampling point in this period. Finally, using the best linear Kalman filter in the sense of LMMSE directly, the sequential filtering fusion of asynchronous sampling measurements in this period can be realized. Compared with the existing typical asynchronous algorithms which depend on equivalent pseudo-measurements, the proposed algorithm has lower computational load, better real-time and accurateness. This paper elaborates the form of this new algorithm, and finally computer simulation demonstrates validity of the new algorithms.