Adaptive Consensus-Based Distributed Target Tracking With Dynamic Cluster in Sensor Networks

This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.