FL-EKF-Based Cooperative Localization Method for Multi-AUVs

Autonomous underwater vehicle (AUV) has been widely used in underwater missions. Cooperative localization (CL) is a key technology especially for multi-AUVs collaborative operations. With great demands for accurate and real-time localization, the error dispersion in nonlinear fusion and information transmission difficulties caused by underwater environment limitations become challenges in multi-AUVs CL. In this article, a federated learning (FL) framework for multi-AUVs CL is designed, based on which a novel CL algorithm combining the FL and extended Kalman filter (EKF) is proposed. The proposed FL-EKF algorithm can fuse the advantages of EKF and FL adequately to realize high-precision real-time underwater CL in long-duration operations. Simulations and experiments are conducted to verify the performance of the proposed algorithm.