ADMOST: UAV Flight Data Anomaly Detection and Mitigation via Online Subspace Tracking

• Published in: IEEE transactions on instrumentation and measurement Vol. 68; no. 4; pp. 1035 - 1044
• Main Authors: He, Yongfu, Peng, Yu, Wang, Shaojun, Liu, Datong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft; Aircraft accidents & safety; Algorithms; Anomalies; Anomaly detection; Anomaly detection (AD); anomaly mitigation; Aviation; Control theory; Data analysis; Data recovery; Error detection; Estimation; flight data; Heuristic algorithms; Multivariate analysis; online subspace learning; Outliers (statistics); Robustness; Subspaces; Tracking control; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vehicle dynamics
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2018.2863499

Since the control laws require sensor feedback to set the current dynamic state of the unmanned aerial vehicle (UAV), incorrect readings may lead to potentially catastrophic conditions. Thus, automated detection and mitigation of UAV flight data anomaly is an important problem in the aviation domain. However, the conventional anomaly detection algorithms simply detect the outlier points and cannot provide estimated values. To address this challenge, anomaly detection and mitigation algorithm based on online subspace tracking, an online algorithm for flight data anomaly detection and mitigation is proposed. At every time instant, data subspace matrix is used as a meaningful data representation of raw multivariate heterogeneous flight data. Besides, in terms of outlier points, the subspace matrix is tracked with incomplete partial observation. Anomaly score is calculated based on the identification of a change in the underlying subspace. Utilizing the tracked subspace matrix, the detected outlier points are replaced with reasonable recovered estimations, which will help mitigate the influence of anomaly to UAV system control. Experimental results on real UAV flight data demonstrate its ability to maintain high accuracy for anomaly detection and low error for data recovery.