Multiple-vehicle distributed weighted consensus state estimation

To address the limitations of traditional distributed average fusion in multiple-vehicle collaborative combat under complex environments, this paper proposes a weighted average consensus algorithm within the alternating direction method of multipliers (ADMM) framework. A matrix representation of the optimization iterations is provided. Based on the estimated states of each platform, the Wasserstein distance is utilized to determine real-time fusion weights. The proposed method achieves distributed weighted average consensus fusion within the ADMM framework. Simulation results demonstrate that the proposed algorithm improves fusion accuracy in complex environments.