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FL-EKF-Based Cooperative Localization Method for Multi-AUVs
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AI
DOI：10.1109/JIOT.2024.3414501.png)
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Abstract
En 中文
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.
Keywords:
Location awareness
Federated learning
Accuracy
Training
Prediction algorithms
Navigation
Real-time systems
Cooperative localization (CL)
extended Kalman filter (EKF)
federated learning (FL)
multi-autonomous under-water vehicle(AUVs)
Journal
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8.9
Papers:
1.4W
Citations:
7.8W