Fined-grained unmanned aerial vehicle anomaly detection by fusing multi-domain features

Abstract Unmanned aerial vehicle (UAV) fault brings about anomalies of sensors data, which can have serious and catastrophic consequences. Therefore, UAV anomaly detection (UAVAD) is crucial for its safe flight. As fail to differentiate the fined-grained feature error and fuse multi-domain features, the existing reconstruction error (RE) based anomaly detection (AD) methods are limited in detection abilities. Besides, the same RE may happen for different data. Targeting at this issue, the fined-grained UAVAD method fusing multi-domain features is proposed, which can realize UAVAD with high accuracy. Firstly, UAV sensors data is preprocessed, time-domain and frequency-domain features are extracted to form multi-domain features. Further, time-domain and frequency-domain features are integrated, and feature selection is performed on the data fused with multi-domain features. Next, the reconstruction model with double decoders is utilized to make feature extraction of UAV sensors data. Then, by considering the fined-grained feature error, under one versus others and hierarchical methods, respectively, we utilize the reconstruction model with double decoders to make AD of multi-class faults of UAV. Finally, the findings from the experiments on ALFA and simulated datasets illustrate that the proposed method works well. Especially, under hierarchical method, the proposed UAVAD method can achieve 100% accuracy for each type of UAV samples.