Trust-Based Distributed Kalman Filtering for Target Tracking under Malicious Cyber Attacks

As one of the widely used applications in wireless sensor networks, target tracking has attracted considerable attention. Although many tracking techniques have been developed, it is still a challenging problem if the network is under cyber attacks. Inaccurate or false information is maliciously broadcast by the compromised nodes to their neighbors. They are likely to threaten the security of the system and result in performance deterioration. In this paper, a distributed Kalman filtering technique with trust-based dynamic combination strategy is developed to improve resilience against cyber attacks. Furthermore, it is efficient in terms of communication load, only local instantaneous estimates are exchanged with the neighboring nodes. Numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks