Robust Kalman filter for systems subject to parametric uncertainties (Q2059479)

In this paper, the authors give a robust Kalman filter for uncertain linear discrete-time systems. They reduce the filter problem to a robust regularized least-squares estimation problem. The filter is given by both in a symmetric matrix arrangement and as explicit algebraic expressions in a Kalman-like structure that is suitable for online applications. They show that the steady-state filter is stable and guarantees a bounded error variance for quadratically stable systems. At last, a numerical example is given. The main novelty of the result in this paper is that there may be some uncertainty in the model.