Distributed robust information filter for Markov jump systems with outliers

This paper studies the problem of distributed state estimation for linear Markov jump systems subject to state and measurement outliers. Initially, a robust local multiple model information filter is developed by integrating the traditional interactive multiple model framework with statistical similarity measure. This local filter is then extended to a distributed version via a diffusion-based information fusion strategy. Furthermore, based on stochastic stability theory, sufficient conditions are derived to ensure the boundedness of estimation errors in the mean square sense. The simulation results demonstrate the effectiveness of the proposed local and distributed multiple model filters in the presence of state and measurement outliers.