An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed...

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Vydáno v:Aerospace Ročník 11; číslo 5; s. 393
Hlavní autoři: Zhou, Shenghan, He, Zhao, Chen, Xu, Chang, Wenbing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2024
Témata:
Accidents
Accuracy
Aerial photography
Algorithms
Analysis
Anomalies
anomaly detection
Clustering
Computational linguistics
Condition monitoring
Data acquisition
Decomposition
Deep learning
Embedded systems
Experiments
Fault diagnosis
Feature extraction
Flight
Flying-machines
Language processing
Machine learning
Methods
Natural language interfaces
Neural networks
Noise reduction
Principal components analysis
Risk reduction
Sensors
stacked denoising autoencoder (SDAE)
Support vector machines
UAV flight data
Unmanned aerial vehicles
wavelet decomposition
Wavelet transforms
ISSN:2226-4310, 2226-4310
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Shrnutí:The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.
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ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace11050393