Distributed multiple model estimation for jump Markov linear systems with missing measurements

This paper is concerned with distributed multiple model estimation for jump Markov linear systems with missing measurements over a sensor network. Two independent Markov chains are used to describe the switching of dynamic models and the missing of measurements, respectively. Under the assumption that each sensor can only communicate with its neighbours, a distributed filter is developed by applying the basic interacting multiple model (IMM) approach in the Bayesian estimation framework. To circumvent the difficulty of exponentially growing filters by exchanging local measurements between neighbouring sensors, the mode-conditioned estimates are exchanged instead of local measurements and the covariance intersection method is adopted to fuse mode-conditioned estimates. A multi-sensor manoeuvering target tracking example is provided to verify the effectiveness of the proposed filter.