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Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

Mwenegoha, Hery A.; Moore, Terry; Pinchin, James; Jabbal, Mark


Hery A. Mwenegoha

Terry Moore


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.


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, 1-14.

Journal Article Type Article
Acceptance Date Apr 21, 2021
Online Publication Date Nov 11, 2021
Publication Date Nov 11, 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 (CUP)
Peer Reviewed Peer Reviewed
Pages 1-14
Keywords Ocean Engineering; Oceanography
Public URL
Publisher URL
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 (, 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.


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