Decentralized target positioning and tracking based on a weighted extended Kalman filter for wireless sensor networks

This paper presents a decentralized positioning and tracking method based on recursive weighted least-squares optimization for wireless sensor networks. The proposed algorithm—weighted extended Kalman filter—is derived by minimizing a recursive-in-time objective function and then applying it in an iterative decentralized manner. The target location is calculated iteratively by taking a weighted average of the local estimates based on the participating sensor nodes’ reliability, where a participating sensor node computes the newest location estimate according to its own observation and the most recent local estimate passed from the previous participating sensor node. A convergence analysis is given to show the convergence behavior of the proposed algorithm. To track the target in the network, a message-passing algorithm is proposed for adaptively selecting the participating sensor nodes as the target moves around the area. During each iteration, the current participating sensor node computes the local estimate and passes it on to the next participating sensor node for further processing. The update process is circulated only among the selected participating sensor nodes that surround the target. Computer simulation results show that our proposed algorithm outperforms previous related methods.