Decentralized estimation of sensor systematic error and target state vector

An accurate estimation of the sensor systematic error is significant for improving the performance of target tracking system. The existing methods usually append the bias states directly to the variable states to form augmented state vectors and utilize the conventional Kalman estimator to achieve state vectors estimate. So doing is expensive in computation, and much work is devoted to decoupling variable states and systematic error. But the decentralied estimation of systematic errors and reduction of the amount of computation as well as decentralied track fusion are far from being realized. This paper addresses distributed track fusion problem in multi-sensor tracking system in the presence of sensor bias. By this method, variable states and systematic error is decoupled. Decentralized systematic error estimation and track fusion are achieved. Simulation results verify that this method can get accurate estimation of systematic error and state vector.