Distributed Robust Dimensionality Reduction Fusion Estimation Under DoS Attacks and Uncertain Covariances

This paper is concerned with the networked distributed fusion estimation problem under denial-of-service (DoS) attacks, where the noise covariances are unknown but bounded, and the distribution information of DoS attacks is not required to be known. Based on the dimensionality reduction and compensation model, the local Kalman filter (LKF) with unknown covariances is designed by the maximum and minimum robust estimation criterion, while the distributed fusion Kalman filter (DFKF) is derived from the optimal weighted fusion criterion. Moreover, the robustness of the developed DFKF is also analyzed in the presence of DoS attacks. Finally, an illustrative example is exploited to demonstrate the effectiveness of the proposed methods.