Robust median consensus cubature Kalman filter for distributed sensor networks

Distributed estimation of multiagent systems has gained increasing attention in recent years, with consensus algorithms being one of the popular research topics. However, most existing algorithms aim to reach a consensus on the average value of all nodes, which can be significantly affected by outliers and disturbances. To achieve a more resilient distributed estimation, this paper focuses on the median value consensus strategy. Specifically, a continuous median consensus method with an adaptive parameter is presented and discretized using timevarying steps. This approach eliminates chattering errors caused by discretization and enhances convergence speed considerably. Furthermore, utilizing the proposed algorithm, a median consensus cubature Kalman filter (MC-CKF) is designed for nonlinear scenarios. Simulation results demonstrate that our proposed algorithm outperforms traditional algorithms when facing a denial -of -service attack.