Consensus-based distributed two-target tracking over wireless sensor networks

This paper studies the target tracking problem over wireless sensor networks (WSNs). While most existing works on this problem focus on the single-target case and the few existing two-target tracking methods are based on linear observation models, we pay attention to the two-target tracking over WSNs, considering nonlinear targets’ dynamics and observation models. We divide all the sensors in the WSN into two groups corresponding to the two targets such that sensors in each group only observe one target but collect the information of the other one by communicating with the sensors in the other group. Then, the consensus-based distributed two-target tracking (CDTT) algorithms are proposed, applying the covariance intersection fusion rule to the information form of the extended Kalman filter. With this fusion rule, the consistency of the algorithms is guaranteed and the estimation accuracy is improved. Through analyzing the boundedness of the information matrices and the Lyapunov candidate defined on the estimation errors, we prove that the two-target tracking task can be completed by running the CDTT algorithms under certain conditions. Moreover, we extend the CDTT algorithms to the multi-target case, obtaining the consensus-based distributed multi-target tracking algorithms and showing their performance analysis. Simulation and experimental results are given to illustrate the performance of these tracking algorithms.