Aircraft Maneuver Intelligent Recognition: An End-to-end Regularized Autoencoder and SVM Approach

Aircraft maneuver recognition is a key issue in unmanned aerial vehicle (UAV) intelligent air combat. Aiming at inefficiency of high-dimensional time-series maneuver data analysis and low recognition accuracy of traditional methods, an end-to-end regularized autoencoder-support vector machine (RAE-SVM) method is proposed. This method combines powerful feature extraction capability of unsupervised learning based autoencoder with superior classification performance of supervised learning based SVM. According to the change rule of maneuver data and prior expert knowledge, the maneuver recognition dataset based on time period feature data is constructed. The generalization performance of RAE network and the accuracy of the model are improved by introducing regularization. The aircraft maneuver recognition model based on RAE-SVM is constructed and verified by the maneuver recognition dataset. The simulation results show that the accuracy of model recognition is as high as 92.75%. The trained model only takes 2 milliseconds to recognize a set of maneuver data and meets the near real-time requirements. Therefore, the proposed approach in this work can quickly and accurately recognize aircraft maneuver without relying on expert experience, which has certain practical value.