Keywords
sensor fault detection, unmanned aircraft, UAV
Abstract
This paper proposes a method of detecting faults in non-redundant sensors. Such a method is advantageous for small unmanned aerial vehicles (UAVs), which are prevented from carrying redundant sensors due to size, weight, and power constraints. The method we propose uses a multiplicative extended Kalman lter (MEKF) for estimation and employs hypothesis testing to detect faults. This method has been shown to detect bias, drift, and increased noise in a non-redundant sensor real-time on board an autonomous rotorcraft.
Original Publication Citation
Brandon Cannon, Robert Leishman, Tim W. McLain, Joseph A. Jackson, and Jovan Boskovic. "Non-Redundant Sensor Fault Detection for Autonomous Rotorcraft using an Improved Dynamic Model", AIAA Guidance, Navigation, and Control (GNC) Conference, Guidance, Navigation, and Control and Co-located Conferences, (AIAA 2013-4776). http://dx.doi.org/10.2514/6.2013-4776
BYU ScholarsArchive Citation
Cannon, Brandon; Leishman, Robert C.; McLain, Timothy W.; Jackson, Joseph; and Boskovic, Jovan, "Non-redundant Sensor Fault Detection Using an Improved Dynamic Model" (2013). Faculty Publications. 1505.
https://scholarsarchive.byu.edu/facpub/1505
Document Type
Peer-Reviewed Article
Publication Date
2013-8
Permanent URL
http://hdl.lib.byu.edu/1877/3432
Publisher
AIAA
Language
English
College
Ira A. Fulton College of Engineering and Technology
Department
Mechanical Engineering
Copyright Status
Cannon, B., Leishman R., McLain, T., Jackson, J., Boscovic, J. Non-redundant Sensor Fault Detection Using an Improved Dynamic Model, Proceedings of the AIAA Guidance, Navigation, and Control Conference, AIAA 2013-4776, August 2013, Boston, Massachusetts. doi: 10.2514/6.2013-4776
Copyright Use Information
http://lib.byu.edu/about/copyright/