UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Adaptive federated filter–combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships

Guo, Muzhuang; Zhou, Xiaomin; Guo, Chen; Liu, Yuanchang; Zhang, Chuang; Bai, Weiwei; (2024) Adaptive federated filter–combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships. Journal of Marine Engineering and Technology 10.1080/20464177.2024.2305721. (In press). Green open access

[thumbnail of Adaptive federated filter combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships.pdf]
Preview
Text
Adaptive federated filter combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships.pdf - Published Version

Download (9MB) | Preview

Abstract

The integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship’s motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems.

Type: Article
Title: Adaptive federated filter–combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/20464177.2024.2305721
Publisher version: http://dx.doi.org/10.1080/20464177.2024.2305721
Language: English
Additional information: Copyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Keywords: Maritime autonomous surface ships; robust; integrated navigation; observability; information allocation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10186809
Downloads since deposit
21Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item