Sequential fusion Kalman filter

For the multisensor linear discrete time-invariant system, the batch fusion (BF) Kalman filtering algorithm needs the inverse operation of a high-dimensional matrix, which yields a larger computational burden and computational complexity. A sequential fusion (SF) Kalman filter is presented in this paper, which can significantly reduce the computational burden. It is equivalent to several two-sensor Kalman fusers weighting by matrices, and is a recursive two-sensor Kalman fuser. It is proved that its accuracy is higher than that of each local estimator and is lower than that of the batch fusion Kalman filter weighted by matrices. The geometric interpretation of accuracy relations based on the covariance ellipses is given. Two simulation examples for multisensor tracking systems show that its actual accuracy is not very sensitive with respect to the orders of sensors, and is close to the accuracy of the optimal batch fusion Kalman filter.