Sensor Fault Detection for Aircraft Using a Single Kalman Filter and Hidden Markov Models

Abstract : This paper presents a new scheme for sensor fault detection and isolation. It uses a single Kalman filter and a Gaussian hidden Markov model for each of the monitored sensors. This combination is able to simultaneously detect single and multiple sensor faults, still guaranteeing optimal system state estimation. This algorithm also can run on systems with limited computational power. The efficiency of the approach is evaluated through simulation of an aircraft to detect airspeed and GPS sensor faults. The results show fast fault detection and low false-alarm rate.
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Communication dans un congrès
IEEE Multi-conference on Systems and Control, Oct 2014, Antibes, France. Proceedings of the IEEE Multi-conference on Systems and Control (MSC), Invited session on UAVs, pp.991 - 996
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https://hal.archives-ouvertes.fr/hal-01301765
Contributeur : Guillaume Ducard <>
Soumis le : mardi 12 avril 2016 - 18:14:00
Dernière modification le : vendredi 9 septembre 2016 - 11:50:22

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  • HAL Id : hal-01301765, version 1

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Rudin Konrad, Guillaume Ducard, Roland Siegwart. Sensor Fault Detection for Aircraft Using a Single Kalman Filter and Hidden Markov Models. IEEE Multi-conference on Systems and Control, Oct 2014, Antibes, France. Proceedings of the IEEE Multi-conference on Systems and Control (MSC), Invited session on UAVs, pp.991 - 996. <hal-01301765>

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