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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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 14
Hlavní autori:	Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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 spatial-temporal characteristics of flight data with noises and disturbances. To this end, this article proposes an autoencoder-based unsupervised anomaly detection framework with multivariate flight data without labeled information. Specifically, we designed a new robust spatial-temporal autoencoder (RSTAE) model based on the temporal convolution network (TCN), the convolution neural network (CNN), and the attention mechanism to extract the complicated spatial-temporal correlations in multivariate flight data. Instead of using the traditional mean square error (mse) loss function, a modified loss function based on maximum correntropy criteria (MCC) is introduced to enhance the robustness of our RSTAE model during its training process. To further improve the anomaly detection performance, a dynamic threshold strategy is used. Experimental results on real flight data demonstrate the superior performance of the proposed method compared with several autoencoder-based methods in terms of six evaluation metrics.
AbstractList	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 spatial-temporal characteristics of flight data with noises and disturbances. To this end, this article proposes an autoencoder-based unsupervised anomaly detection framework with multivariate flight data without labeled information. Specifically, we designed a new robust spatial-temporal autoencoder (RSTAE) model based on the temporal convolution network (TCN), the convolution neural network (CNN), and the attention mechanism to extract the complicated spatial-temporal correlations in multivariate flight data. Instead of using the traditional mean square error (mse) loss function, a modified loss function based on maximum correntropy criteria (MCC) is introduced to enhance the robustness of our RSTAE model during its training process. To further improve the anomaly detection performance, a dynamic threshold strategy is used. Experimental results on real flight data demonstrate the superior performance of the proposed method compared with several autoencoder-based methods in terms of six evaluation metrics.
Author	Jiang, Guoqian Li, Yingwei Nan, Pengcheng Zhang, Jingchao Li, Xiaoli
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SubjectTerms	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
Title	Robust Spatial-Temporal Autoencoder for Unsupervised Anomaly Detection of Unmanned Aerial Vehicle With Flight Data
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