Cooperative Tracking of Multiple USVs System Based on Robust EKF and Weighted Average Consensus

In this paper, a cooperative tracking algorithm for multiple unmanned surface vehicles (USVs) system is presented. The proposed method addresses the challenge of target state estimation when only the line-of-sight (LOS) angle and its rate are available, without direct range measurements. First, a strapdown guidance formulation is adopted to model the relative motion between the USV and the target, from which a robust extended Kalman filter (EKF) is developed to overcome observability limitations and mitigate the impact of measurement noise and outliers. Second, a multi-USV cooperative estimation strategy is introduced by integrating a weighted-average consensus mechanism into the robust EKF, enabling decentralized yet accurate target state estimation. Finally, the feasibility and stability of the proposed cooperative tracking algorithm are theoretically analyzed and validated through numerical simulations. Simulation results demonstrate that the method achieves fast convergence, high estimation accuracy, and strong robustness under realistic sensor error conditions.