Secure Distributed Unscented Kalman Filter Against Cyber Attacks: DUKF with cluster-based attack detection strategy

During the past decades, distributed unscented Kalman filter (DUKF) has developed rapidly in the field of position tracking in sensor networks. In this paper, we propose a DUKF with cluster-based attack detection strategy algorithm for state estimation under malicious network attacks. The application of the attack detection algorithm with two layers of cycling mechanism to the state estimation attack detection guarantees the security and reliability of the converged data. A good estimation of the situation in which the information of the secure node is unknown is achieved by measuring 4 steps: update, cluster-based attraction detection, information fusion and prediction. Moreover, the simulation results show that the method is robust to random attacks, FDI attacks, reverse attacks and delayed attacks.