Robust Sequential covariance intersection fusion Kalman filters with noise variances uncertainties

This paper consider the robust covariance intersection fusion Kalman filtering problems for steady-state multi-sensor systems with noise variances uncertainties. Based on the conservative upper bounds of noise variances, a fast sequential covariance intersection (SCI) robust Kalman filter is presented, which only require solving the optimization of several one-dimension nonlinear cost functions. And then the robust accuracy relations of these robust Kalman filters are proved. It is proved that the robust accuracies of SCI robust Kalman filters are all higher than that of each local robust Kalman filter. The geometric interpretation of the accuracy relations is given based on the variance ellipses. A simulation example verifies the robustness and the correctness of the theoretical accuracy relations.