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.

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

“…Meanwhile, since trajectory prediction needs to be based on a priori information, real-time continuous tracking of the HGV motion state is also a problem that needs to be solved for trajectory prediction. Currently, HGV trajectory prediction methods can be broadly classified into two categories: motion model-based prediction methods [11] and data-based prediction methods [12]. The former investigates the change process of target trajectory with control quantity from the target motion control perspective.…”

A New Intelligent Recognition Method for Maneuver Modes of Re-entry Gliding Vehicle

“…In addition, the use of neural networks in deep learning to achieve maneuver mode classification of time series based on target state information is proved feasible [8][9] . Li et al [10] propose a set of parameters suitable for maneuver mode recognition and parameter estimation based on typical maneuver modes of targets. Using long and short-term memory networks (LSTM), an intelligent trajectory prediction structure based on maneuver mode recognition is proposed.…”

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

“…Several filtering schemes have been developed to cope with the lateral manoeuvres of MaRVs [18][19][20][21][22][23][24][25]. However, they have mainly focused on the accuracy of target tracking rather than target identification.…”

“…Meanwhile, in recent studies, the prior knowledge-based interacting multiple-model filter has been applied to hypersonic reentry vehicle tracking [22][23][24]. This method constructs multiple target motion models by considering the angle of attack and the bank angle as crucial parameters that determine the HGV's manoeuvres.…”