Liu, Wenwen;
Liu, Yuanchang;
Bucknall, Richard;
(2022)
Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation.
Journal of Marine Engineering & Technology
, 22
(2)
pp. 67-83.
10.1080/20464177.2022.2031558.
Preview |
Text
Liu_Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation.pdf Download (5MB) | Preview |
Abstract
When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered.
| Type: | Article |
|---|---|
| Title: | Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/20464177.2022.2031558 |
| Publisher version: | https://doi.org/10.1080/20464177.2022.2031558 |
| Language: | English |
| Additional information: | © 2022 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-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142818 |
Archive Staff Only
 |
View Item |
