Event-Triggered Distributed Fusion Estimation for Networked Systems with One-Step Random Delays and False-Data-Injection Attacks

In this paper, an secure event-triggered distributed fusion filter (ETDFF) based on the probabilities of random delays and networked attacks is designed. In the considered system, there exist random one-step observation delays and false data injection (FDI) attacks. The send-on-delta (SoD) event-triggered mechanism is adapted to alleviate the bandwidth pressure. Based on the state augmentation method, the optimal linear Kalman local filters and distributed fusion filtering algorithm in the sense of linear minimum variance (LMV) are derived. The validity and superiority of the proposed algorithms have been verified through the simulation example.