| Citation: | ZHONG J，LUO C，ZHANG H，et al. Flight data anomaly detection based on correlation parameter selection[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（5）：1738-1745 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0574
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With the maturity of unmanned aerial vehicle (UAV) technology, UAVs are being used more and more widely in the military and civilian fields. Meanwhile, the safety of UAV is gradually being emphasized. The health of the UAV’s flight can be immediately reflected in its flight data. Anomaly detection research for UAV flight data is one of the important ways to improve the overall safety of UAVs. In this paper, we propose a convolutional neural network (CNN) anomaly detection method based on correlation parameter selection for flight data. Firstly, we use the maximal information coefficient (MIC) and Pearson correlation coefficient method to explore the correlation among flight parameters and establish a set of correlations between flight parameters. Then, we use the correlation flight parameters to train the convolutional neural network regression model. Finally, the anomalies were determined based on the residuals between the true and predicted values of the model. The false positive rate, false negative rate, and accuracy indexes of the approach suggested in this work were 0%, 0.19%, and 99.6%, respectively, demonstrating the method’s superiority. The method was confirmed using actual UAV flight data.
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