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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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 14
Main Authors: Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3428649