Mobile target tracking and data fusion using dual-interacting multiple model system

In this paper, a cooperative mobile target estimation approach based on interacting multiple model (IMM) algorithm is presented. We propose a dual-IMM estimator structure to improve the accuracy and robustness of mobile target localization and tracking in wireless sensor networks. Suppose that two sensor systems are affected by different levels of noises, the measured data can be first processed at each individual IMM-based estimator. Each IMM-based estimator then exchanges the local estimates, local model probabilities and model transition probabilities with the other estimator for data sharing and data integration. By updating the associated model probabilities in each of the IMM estimators, the dual structure performs state estimation and attains the objective of data fusion for target tracking. Simulation results show that the overall performance of the dual-IMM estimator is improved. The proposed dual-IMM estimator structure can also be extended to multiple-IMM cases for data fusion, cooperative localization and target tracking.