Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery

Multi-source data fusion-based knowledge transfer for unmanned aerial vehicle flight data anomaly detection and recovery

Abstract Flight data anomaly detection (AD) is essential for unmanned aerial vehicle (UAV) health management. Despite the current dominance of data-driven approaches, their effectiveness often requires sufficient data for model training. However, in practice, it is inevitable to face the situation o...

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Bibliographic Details
Main Authors: Lei Yang, Shaobo Li, Liya Yu, Caichao Zhu, Congbao Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Unmanned aerial vehicle (UAV)
Anomaly detection (AD)
Data recovery
Transfer learning
Multi-source data fusion
Online Access:https://doi.org/10.1038/s41598-025-05322-4
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https://doi.org/10.1038/s41598-025-05322-4

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