Stochastic Event-Triggered Distributed Fusion Estimation Under Jamming Attacks

Abstract

The paper concentrates on the distributed fusion estimation issue of a bandwidth-constrained multi-sensor nonlinear networked system suffering from jamming attacks. For each communication channel, a stochastic event-triggered transmission scheme is developed to reduce excessive communication between smart sensors and local estimators, and a Stackelberg game framework is established to analyze interactions between the smart jammer and smart sensors. Utilizing a sequential fast covariance intersection fusion rule, a distributed fusion estimation algorithm is designed by fusing local estimations from event-triggered unscented Kalman filter-based local estimators. Then convergence conditions are derived by analyzing behaviors of the fusion estimation error covariance, and the boundedness of communication rate for each communication channel is further discussed. Finally, a comparative simulation is given to testify the validity of the proposed fusion technique.