Distributed information fusion Kalman predictor for stochastic systems with uncertain observations

In sensor networks, sensor measurements may be uncertain due to the impact of environment and different performances of sensors. In this paper, the cross-covariance matrix of prediction errors between any two sensor subsystems is derived for stochastic discrete-time linear systems with uncertain observations by using projection theory. Based on the linear minimum variance weighted fusion algorithm, the distributed information fusion Kalman predictor is obtained for stochastic systems with uncertain observations. It avoids the high-dimensional computation resorting to state augmentation, and has the better reliability. The simulation example verifies the effectiveness of the algorithm.