Distributed energy‐efficient target tracking algorithm based on event‐triggered strategy for sensor networks
Ya Zhang, Lingling Zhang, Lishuang Du, Cheng‐Lin Liu, Yang‐Yang Chen · IET Control Theory and Applications · 2019
The distributed estimation problem for wireless sensor networks with limited communication/sensing ranges and observability is studied. A novel sensor measuring activation scheme based on a fully distributed event‐triggered strategy is proposed to make each node achieve a better trade‐off between estimation error and energy saving. The strategy depends on both the predicted synthetic performance index and the predicted position of the target. A distributed Kalman filtering algorithm based on the minimum trace fusion principle is proposed. It is proved that comparing with the time‐triggered strategy, the proposed event‐triggered measuring strategy has better performance. Although the event‐triggered measuring topology is time‐varying and each sensor is not observable, it is proved that as long as there exists at least one collaboratively observable sensor in the available distance‐based sensing network at each time instant, the estimation errors are bounded in mean square sense. Simulation examples are given to illustrate the validity of the algorithm.