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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| Published in: | IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 14 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| 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. |
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| 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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