@inproceedings{0f2035d199aa48c78aa28f787bca136e,
title = "Cooperative Navigation for Multi-AUVs Based on the Strong Tracking Filter and Embedded Cubature Kalman Filter",
abstract = "Cooperative navigation is regarded as a promising positioning technology for autonomous underwater vehicles (AUVs). This paper presents a modified method based on the Strong Tracking Filter (STF) and Embedded Cubature Kalman Filter (ECKF) for multi-AUV cooperative navigation. This method applies the STF to the traditional ECKF algorithm, effectively diminishing the error generated in the process of navigation when confronting the challenges of uncertain system models or system mutations. Simulations compare the modified method with two related traditional navigation methods and verily that our method maintains the highest positioning accuracy and stability under the adverse condition of system mutation. Compared with the ECKF algorithm, the positioning and heading angle errors derived from the modified method are reduced by 72.28\% and 51.52\%.",
keywords = "Autonomous Underwater Vehicle, Cooperative Navigation, Embedded Cubature Kalman Filter, Strong Tracking Filter",
author = "Qinghua Luo and Yanyi Chen and Xiaozhen Yan and Yang Shao",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022 ; Conference date: 13-10-2022 Through 16-10-2022",
year = "2022",
doi = "10.1109/PHM-Yantai55411.2022.9942125",
language = "英语",
series = "2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li",
booktitle = "2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022",
address = "美国",
}