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

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles (UAVs) and has attracted extensive attention from scholars. Knowledge-based approaches rely on prior knowledge, while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems (UASs). Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models, they often lack parameter selection and are limited by the cost of labeling anomalous data. Furthermore, flight data with random noise pose a significant challenge for anomaly detection. This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder (STC-LSTM-AE) neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data. First, UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model. Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge. Then, the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner. Finally, the method’s effectiveness is validated on real UAV flight data.