.png){kind=icon}
返回
FL-EKF-Based Cooperative Localization Method for Multi-AUVs
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2024.3414501.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
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)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
K-FL: Kalman Filter-Based Clustering Federated Learning MethodK-fl: 基于卡尔曼滤波的聚类联邦学习方法.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments
PREAI
分享收藏On-Device Indoor Positioning: A Federated Reinforcement Learning Approach With Heterogeneous Devices设备上室内定位: 一种基于异构设备的联合强化学习方法
PREAI
分享收藏AUV-Aided Localization for Internet of Underwater Things: A Reinforcement-Learning-Based Method
PREAI
分享收藏
查看更多内容