Dynamic collaborative algorithm for maneuvering target tracking in sensor networks

A distributed dynamic clustering and collaborative tracking algorithm is proposed for maneuvering target tracking problems in sensor networks. The sensor node is selected adaptively and a sensor cluster is activated online by optimizing the performance measure of tracking and cost of communication. Accuracy of tracking is improved by dynamic collaboration and information fusion of the sensor nodes. The particle filtering is employed to predict and estimate the probability distribution of target states due to nonlinear problems and randomness of the sensor nodes. The Gaussian mixture particle filtering and the shortest routing algorithm are utilized for information exchange between the sensor nodes to save energy of communication. An efficient particle method is proposed for approximating expected posterior mean square error to optimize sensor selection. The simulation shows significant improvement of the proposed algorithm over existing IDSQ methods in tracking accuracy for maneuvering target.