Event Triggered Fractional-Order Distributed Kalman Filtering with Random Deception Attacks

This paper investigates the issue of random deception attacks in distributed systems and proposes an event-triggered fractional-order distributed Kalman filtering method to ensure accurate and robust system state estimation. In fields such as intelligent manufacturing, intelligent transportation, and environmental monitoring, the role of distributed systems is becoming increasingly important. However, with increasing system openness, network security issues, especially random deception attacks, pose a threat to system security and reliability. The article initially establishes a model of network communication topology and provides a detailed description of attack models. Subsequently, the proposed filtering method is introduced, and the upper bound of the estimation error covariance is derived using the variance constraint approach. In addition, a filter gain is designed and numerical simulations are performed to verify the effectiveness of the proposed method against random deception attacks. Finally, the research findings are summarized and future research directions are identified.