TY - GEN
T1 - Maneuver Trajectory Identification for Non-cooperative HGVs Using Support Vector Machine and Evidence Theory
AU - Cheng, Yunpeng
AU - Yan, Xiaodong
AU - Tang, Shuo
AU - Sun, Chengzhi
AU - Wu, Manqiao
N1 - Publisher Copyright:
© 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The maneuver trajectory identification method of the non-cooperative hypersonic glide vehicle (HGV) is optimized by the support vector machine (SVM) and the Dempster-Shafer (D-S) evidence theory using the unscented Kalman filter (UKF) in this study. Firstly, due to the complexity of penetration maneuvers and the uncertainty of maneuvering modes, the maneuver characteristics of the HGV are analyzed from the point of view of trajectory design. Secondly, the stable tracking of the HGV is achieved by the UKF with an established trajectory tracking model of single ground-based radar. Finally, the HGV maneuver trajectory identification framework is completed using SVM and D-S evidence theory. Simulation results show that this optimized method improves the accuracy of HGV’s trajectory parameter identification.
AB - The maneuver trajectory identification method of the non-cooperative hypersonic glide vehicle (HGV) is optimized by the support vector machine (SVM) and the Dempster-Shafer (D-S) evidence theory using the unscented Kalman filter (UKF) in this study. Firstly, due to the complexity of penetration maneuvers and the uncertainty of maneuvering modes, the maneuver characteristics of the HGV are analyzed from the point of view of trajectory design. Secondly, the stable tracking of the HGV is achieved by the UKF with an established trajectory tracking model of single ground-based radar. Finally, the HGV maneuver trajectory identification framework is completed using SVM and D-S evidence theory. Simulation results show that this optimized method improves the accuracy of HGV’s trajectory parameter identification.
KW - Evidence theory
KW - Hypersonic glide vehicle
KW - Maneuver trajectory identification
KW - Support vector machine
KW - Trajectory estimation
UR - https://www.scopus.com/pages/publications/85120629548
U2 - 10.1007/978-981-15-8155-7_354
DO - 10.1007/978-981-15-8155-7_354
M3 - 会议稿件
AN - SCOPUS:85120629548
SN - 9789811581540
T3 - Lecture Notes in Electrical Engineering
SP - 4263
EP - 4274
BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Yu, Xiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2020
Y2 - 23 October 2020 through 25 October 2020
ER -