Aircraft maneuver recognition based on autoencoder support vector machine

DOI:	10.11809/bqzbgcxb2024.02.027
Keywords:	maneuver recognition; regularized auto encoder; support vector machines; feature extraction; unsupervised learning
Abstract:	Aircraft maneuver recognition is a foundation in air combat intention recognition and intelligent decision making. Aiming at the problems of high dimensional data processing and feature extraction ability and low recognition accuracy of traditional maneuver recognition methods, in view of the high dimensionality and time series characteristics of maneuver data, a novel regularized auto encoder support vector machines (RAE SVM) based method is proposed. According to the change law of maneuvering action data and the expert prior knowledge, the maneuver recognition sample library based on time period data features is constructed. Combining powerful feature extraction capability of unsupervised auto encoder with superior classification performance of supervised support vector machine, the aircraft maneuver recognition model based on RAE SVM is constructed and verified by the maneuver recognition sample library. The generalization performance and the accuracy of RAE network are improved by introducing regularization. The simulation results show that the recognition accuracy of the proposed method is 92.75%. The trained model takes 2 milliseconds to recognize a set of maneuver data and the running time meets the real time requirements. Therefore, the proposed method can quickly and accurately recognize aircraft maneuver, which has certain practical value.
Issue:	Vol. 45 No. 2 (2024)
Published:	2024-02-28
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