sensor fault detection, unmanned aircraft, UAV
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). All Faculty Publications. 1505.
Ira A. Fulton College of Engineering and Technology
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