Unscented Information Consensus Filter for Maneuvering Target Tracking Based on Interacting Multiple Model

This paper deals with the problem of maneuvering target tracking with networked multiple sensors. To avoid linearization of nonlinear function, and obtain more accurate estimate for maneuvering target, a novel distributed maneuvering target tracking method based on interacting multiple model with unscented information consensus protocol is proposed. The pseudo measurement matrix is computed according to unscented transform, based on which the information form of measurements is calculated and local estimate is updated. To unify estimation in different sensors and improve the maneuvering target tracking accuracy throughout the whole network, the weighted information consensus protocol is applied for each model in all sensors. With multiple models interacting, the posterior estimate in each sensor is acquired with weighted combination of the model-conditioned estimates. Experimental results demonstrate that the proposed algorithm outperforms the existing methods in the aspect of tracking accuracy and agreement of estimates in all sensors.

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