Event-Based Distributed Filtering for Multitarget Tracking Systems with Coupled Measurements and Cyberattacks

In this study, the event-triggered distributed Kalman consensus filtering problem for multitarget tracking systems vulnerable to malicious attacks is the main topic. Based on the directed network topology, the coupled measurement that relies on target states from incoming neighborhoods is utilized to estimate the target state. Considering the coupling features of measurements, an augmented-system method is introduced to reconstruct the system model. Due to the constraints of communication bandwidth and vulnerability of the communication network between filters, an event-based distributed Kalman consensus filter (DKCF) is designed under cyberattacks. For scalability considerations, a suboptimal DKCF is derived by simplifying the optimal one. To analyze the tracking performance of the proposed suboptimal DKCF, a sufficient condition is devised to ensure its stability, and the consensus gain matrix is subsequently obtained. Moreover, the relationship between the attack vector and the event-triggered method is established, and a feasible event condition in a practical environment is derived. Finally, to confirm that the event-based DKCF mentioned above is effective, a multitarget tracking system simulation example is provided.