Robust Spatial-Temporal Autoencoder for Unsupervised Anomaly Detection of Unmanned Aerial Vehicle With Flight Data

Recently, the safety and reliability of unmanned aerial vehicles (UAVs) have gained increasing interest, and data-driven anomaly detection methods have been widely studied. However, it is still challenging to build an accurate and reliable detector due to the scarcity of labeled data and complex spa...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 14
Hauptverfasser:	Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection Artificial neural networks Autonomous aerial vehicles Convolution Correlation Data models Feature extraction Flight Flight training Multivariate analysis Multivariate flight data Performance evaluation robust spatial-temporal autoencoder (RSTAE) Robustness Spatiotemporal data Training unmanned aerial vehicle (UAV) Unmanned aerial vehicles unsupervised anomaly detection Unsupervised learning
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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