An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification

Abstract

Trajectory prediction for hypersonic glide targets is a difficult task that needs to be solved. To improve the prediction precision for hypersonic glide targets, based on the analysis of the target’s maneuver characteristic, an intelligent trajectory prediction algorithm based on the maneuver mode identification is proposed in this paper. Firstly, according to the typical maneuver modes of the target, a group of parameter suitable for maneuver mode identification and parameter estimation is proposed. The proposed maneuver parameters can reflect the maneuvering characteristics of the target than the other control parameters. Then, the rationality of parameters is analyzed. Secondly, using the long-short-term memory network (LSTM), the structure of intelligent trajectory prediction based on maneuver mode identification is proposed. The proposed prediction method is designed to improve the prediction accuracy by combining the target dynamic model with the flight data. Finally, the maneuver trajectory data set is established to train and test the method. For the test data set, when the observation time for the target is 200 s and the prediction time is 150 s, with a fast prediction speed, our method’s average error of spatial distance (AESD) is less than 2.9 km, and the maximum error of spatial distance (MESD) is less than 6.9 km. The result is better than other compared mainstream methods. And it is also proved valid with some observational error.