Robust Kalman track fusion in target tracking with uncertainties

In this paper, we consider the robust Kalman filtering based track fusion problem in multi-sensor network. We deal with the dynamic systems when the convariance of the measurement noises suffers norm-bounded uncertainties and propose a minimax robust track fusion method by minimizing the worst-case fusion error variance for all feasible noises covariance matrix. The numerical simulations demonstrate the performance of our method in the dynamic systems with uncertain noise covariance, which outperforms that of the nominal Kalman target tracking fusion method.