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Anomaly Detection Method for Drone Navigation System based on Deep Neural Network
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- Authors
- Seong-Hun Seo, Hoon Jung
- Issue Date
- 2022-06
- Citation
- Journal of Positioning, Navigation, and Timing, v.11, no.2, pp.109-117
- ISSN
- 2288-8187
- Publisher
- 항법시스템학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- This paper proposes a method for detecting flight anomalies of drones through the difference between the command of flight controller (FC) and the navigation solution. If the drones make a flight normally, control errors generated by the difference between the desired control command of FC and the navigation solution should converge to zero. However, there is a risk of sudden change or divergence of control errors when the FC control feedback loop preset for the normal flight encounters interferences such as strong winds or navigation sensor abnormalities. In this paper, we propose the method with a deep neural network model that predicts the control error in the normal flight so that the abnormal flight state can be detected. The performance of proposed method was evaluated using the real-world flight data. The results showed that the method effectively detects anomalies in various situation.
- KSP Keywords
- Control error, Control feedback, Deep neural network(DNN), Detection Method, Feedback Loop, Flight data, Real-world, anomaly detection, flight controller, navigation sensors, navigation system