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Computation-Efficient Centralized Fusion Estimation with Packet Dropouts

This paper is concerned with the computation-efficient centralized fusion Kalman filter for networked multi-sensor fusion systems with packet dropouts. Packet dropouts of each sensor channel is modeled by a Bernouli stochastic variable which is mutually uncorrelated and independent of the process noise and the measurement noises. The sum of the matrix weighted innovation of all the sensor measurements are used to update the one-step predicted state. Then, the computation-efficient centralized fusion Kalman filter is designed by solving a set of discrete-time Riccati equations. An illustrative example is used to show the effectiveness of the proposed algorithm.