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Model-based autonomous navigation with moment of inertia estimation for unmanned aerial vehicles

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


Hery Mwenegoha

Terry Moore

James Pinchin

Mark Jabbal


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

Journal Article Type Article
Publication Date Jun 1, 2019
Journal Sensors (Switzerland)
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 19
Issue 11
Article Number 2467
APA6 Citation Mwenegoha, H., Moore, T., Pinchin, J., & Jabbal, M. (2019). Model-based autonomous navigation with moment of inertia estimation for unmanned aerial vehicles. Sensors, 19(11),
Keywords Electrical and Electronic Engineering; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry
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