Hery A. Mwenegoha
Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles
Mwenegoha, Hery A.; Moore, Terry; Pinchin, James; Jabbal, Mark
Authors
Terry Moore
JAMES PINCHIN JAMES.PINCHIN@NOTTINGHAM.AC.UK
Assistant Professor
MARK JABBAL Mark.Jabbal@nottingham.ac.uk
Associate Professor
Abstract
The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.
Citation
Mwenegoha, H. A., Moore, T., Pinchin, J., & Jabbal, M. (2021). Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles. Journal of Navigation, 74(6), 1353-1366. https://doi.org/10.1017/S0373463321000424
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 21, 2021 |
Online Publication Date | Nov 11, 2021 |
Publication Date | Nov 23, 2021 |
Deposit Date | Nov 25, 2021 |
Publicly Available Date | Nov 25, 2021 |
Journal | Journal of Navigation |
Print ISSN | 0373-4633 |
Electronic ISSN | 1469-7785 |
Publisher | Cambridge University Press |
Peer Reviewed | Peer Reviewed |
Volume | 74 |
Issue | 6 |
Pages | 1353-1366 |
DOI | https://doi.org/10.1017/S0373463321000424 |
Keywords | Ocean Engineering; Oceanography |
Public URL | https://nottingham-repository.worktribe.com/output/6788311 |
Publisher URL | https://www.cambridge.org/core/journals/journal-of-navigation/article/error-characteristics-of-a-modelbased-integration-approach-for-fixedwing-unmanned-aerial-vehicles/DA1121B7019F7B65E6284D880FCF922C |
Additional Information | Copyright: Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation.; License: This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.; Free to read: This content has been made available to all. |
Files
error-characteristics-of-a-model-based-integration-approach-for-fixed-wing-unmanned-aerial-vehicles
(1.7 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Development of a smoke visualisation system for wind tunnel laboratory experiments
(2013)
Journal Article
An aerial deployed unmanned glider for cross-Channel flight
(2015)
Journal Article
Towards the noise reduction of piezoelectrical-driven synthetic jet actuators
(2017)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search