Application of Event-Triggered Cubature Kalman Filter for Remote Nonlinear State Estimation in Wireless Sensor Network

Abstract

This article proposes a novel filter, which is based on the event-triggered schedule and the cubature Kalman filter (CKF), to perform the nonlinear remote state estimation in the wireless sensor network with communication constraints. For the purpose of relieving the communication burden between sensors and the remote filter, the event-triggered schedule is utilized to determine whether the current observations should be sent. To deal with the non-Gaussian property due to the nonlinear transformation, the third-degree spherical-radial cubature rule is adopted to provide the accurate estimation. Moreover, the stochastic stability of the designed event-triggered CKF (ETCKF) is analyzed deriving the relationships among the estimation performance, the communication rate and the design parameter. Therefore, the proposed ETCKF can effectively balance the communication burden and the estimation accuracy. Finally, the numerical simulation on IEEE-39 bus system and unmanned aerial vehicles remote attitude monitoring system are performed to verify the practicability of ETCKF.