2023
DOI: 10.1007/s11431-022-2312-8
|View full text |Cite
|
Sign up to set email alerts
|

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2024
2024
2025
2025

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 13 publications
(2 citation statements)
references
References 33 publications
0
2
0
Order By: Relevance
“…Li et al [29] proposed a hybrid model siamese neural network of CNN and LSTM and verified the proposed method with good fault diagnosis performance on the dataset of literature [18]. Aiming at the problem of anomaly data labeling limitation, Lei et al [30] proposed an unsupervised UAV anomaly detection method based on LSTM and autoencoder and achieved an accuracy of 98.75%. Ensemble learning can be achieved by combining multiple models thereby improving the accuracy and robustness of the models [31].…”
Section: Introductionmentioning
confidence: 95%
“…Li et al [29] proposed a hybrid model siamese neural network of CNN and LSTM and verified the proposed method with good fault diagnosis performance on the dataset of literature [18]. Aiming at the problem of anomaly data labeling limitation, Lei et al [30] proposed an unsupervised UAV anomaly detection method based on LSTM and autoencoder and achieved an accuracy of 98.75%. Ensemble learning can be achieved by combining multiple models thereby improving the accuracy and robustness of the models [31].…”
Section: Introductionmentioning
confidence: 95%
“…The experimental results on real high-dimensional datasets demonstrate the method's excellent performance. Yang et al [42] proposed the STC-LSTM-AE method, a spatiotemporal correlation neural network based on LSTM and autoencoder, for unsupervised anomaly detection and the recovery of UAV flight data. The model uses the Savitzky-Golay filtering technique to reduce sensitivity to data noise.…”
Section: Introductionmentioning
confidence: 99%