UAV flight data is an important state parameter reflecting its own flight safety,and it is a key initiative to improve the overall flight safety of UAVs through abnormal detection of flight data.Although data-driven methods do not require expert a priori knowledge and accurate physical models,the lack of parameter selection and a single model for the detection network structure make the detection model overfitting due to too many parameters and failing to effectively capture data anomaly patterns.In this paper,a VAE-LSTM based UAV flight data anomaly detection modeling method is proposed by combining the advantages of Variational Auto-Encoders and Long Short-Term Memory networks.First,the Kendall correlation analysis method is introduced for selecting relevant dependent flight data parameter sets;Second,the parameter sets with correlation are trained on the designed VAE-LSTM deep hybrid model to learn the relational mapping between different data features;And lastly,the validation is performed with unsupervised anomaly detection in real multi-dimensional Unmanned Aerial Vehicle flight data.The experimental results show that the various average performance metrics of precision,detection rate,accuracy,F1 score and false detection rate of VAE-LSTM reach 95.24％,98.71％,98.8％,96.82％,and 1.31％,respectively,and show overall better anomaly detection performance compared to KNN,OC-SVM,VAE,and LSTM models.
维普
万方数据