Trajectory prediction of hypersonic glide vehicle based on SVM and EKF

被引：0

作者：

Cheng Y. ^{[1
]}

Sun C. ^{[1
]}

Yan X. ^{[1
,2
]}

机构：

[1] School of Astronautics, Northwestern Polytechnical University, Xi'an

[2] Shaanxi Aerospace Flight Vehicle Design Key Laboratory, Xi'an

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 11期

关键词：

Hypersonic Glide Vehicle (HGV); Maneuver mode; Motion recognition; Support Vector Machine (SVM); Trajectory estimation and prediction;

D O I：

10.13700/j.bh.1001-5965.2020.0050

中图分类号：

学科分类号：

摘要：

In the scenario of intercepting a Hypersonic Glide Vehicle (HGV), the trajectory prediction is a key issue for successful interception. Considering HGV's strong maneuverability and variable trajectory, in this paper, a novel trajectory prediction method is proposed based on Support Vector Machine (SVM) and Extended Kalman Filter (EKF). First, the investigation on the maneuvering mode is performed. The maneuver motion of the HGV is divided into longitudinal mode and lateral mode, which are labeled and formulated into the training set of SVMs. Second, the tracking model of the trajectory for single ground-based radar is established, and EKF is applied to track the glide trajectory of HGV. Finally, the recognition framework of HGV motion is established based on SVM, and the prediction of the subsequent trajectory is accomplished. The results show that the proposed method can improve the trajectory prediction accuracy of HGV. © 2020, Editorial Board of JBUAA. All right reserved.

引用

页码：2094 / 2105

页数：11

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