Distributed Multiple Model Filtering for Markov Jump Systems with Measurement Outliers

In this article, a distributed filtering problem is studied for a Markov jump system over sensor networks, where measurements are partially disturbed by outliers. A local multiple model filter is designed based on variational Bayesian approaches and interacting multiple model methods, the designed filter is able to identify and exclude outliers automatically, so as to mitigate the impact of outliers. A distributed filter is proposed by combining the designed local filter with consensus on information methods. Furthermore, a sufficient condition is given to guarantee the stability of the designed distributed filter, in which the estimation errors of each sensor are bounded in the mean square sense. Finally, both simulations and experiments of target tracking systems are done to show the effectiveness of the designed distributed filter.