Robust Cooperative Localization of Autonomous Underwater Vehicle with Derived Adaptive Regularization

This work addresses the single beacon cooperative localization for autonomous underwater vehicles. The unreliable acoustic communication channel is used to obtain the relative range measurements that suffer from outliers, which may lead to casual failure with divergence. To tackle this issue, an adaptive moving horizon estimation framework is proposed for robust localization. Based on the single beacon cooperative localization model and moving horizon estimation, the fixed-dimensional optimization problem is formulated in terms of two adaptive regularizations, the observability one and the indirect velocity one. The uncertainty of position represented by covariance is added to observability regularization, it will be adaptively modified according to the observability of state to adjust the weight of objective function. Physical constraints are considered in the indirect velocity regularization through available proprioceptive measurements. To obtain the covariance and realize fast convergence, the real-time estimator is designed to fastly calculate the initial suboptimal estimation of the optimization problem. Numerical simulations demonstrate that the localization performance of the proposed adaptive estimation is more robust.