Event-triggered optimal Kalman consensus filter with upper bound of error covariance

作者：Li, Zeming; Liu, Yonggui^*; Hu, Xiaoqing^*; Dai, Wenfeng

来源：SIGNAL PROCESSING, 2021, 188: 108175.

DOI：10.1016/j.sigpro.2021.108175

• Event-triggered
• Kalman consensus filter
• Wireless sensor network
• Target tracking

In this paper, we present an event-triggered optimal Kalman consensus filter (EOKCF) for state estimation in distributed wireless sensor networks. The local message is exchanged only when the local estimation violates a predetermined Send-on-Delta (SoD) data transmission condition in order to reduce the amount of data transfer. The neighboring measurement and estimation information are integrated into the consensus filter, and a recursive upper bound of the error covariance is derived for uncertainties of interval communication from the event-based systems. The optimal Kalman gain and consensus gain matrices are obtained by minimizing the upper bound of state error covariance. The Crame? r & minus;& minus;Rao lower bound (CRLB) of the state estimates is also introduced to measure the mean-square estimation error performance. The effectiveness of event-triggered communication protocol and the advantages of filtering performance of EOKCF are demonstrated using Monte Carlo simulations on a target tracking application in a distributed sensor network.