Covariance Intersection Fusion Robust Steady-State Kalman Smoother for Multisensor System with Uncertain Noise Variances

This paper deals with the problem of designing covariance intersection fusion robust steady-state Kalman smoother for multisensor system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust steady-state Kalman smoothers are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of CI fuser is higher than that of each local robust Kalman smoother. A Monte-Carlo simulation of three sensors tracking system verifies their robustness and robust accuracy relations.