Adaptive Robust Kalman Filtering via Krein Space Estimation

This paper is concerned with the design of an adaptive robust Kalman filter for the discrete-time system with norm-bounded parametric uncertainty. A set of covariance-dependent forgetting factors is introduced into the design of robust filtering algorithm in Krein space. Here, the forgetting is non-uniform in time and in space. It is shown, via a simulation example, that the proposed filter achieves robustness against parameter variation and improvement in performance when compared with a conventional Kalman filter and an existing robust Kalman filter, respectively