Re-entry gliding vehicle trajectory prediction based on maneuver detection

doi:10.23919/JSEE.2026.000014

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (1): 9-17.doi: 10.23919/JSEE.2026.000014

• PERCEPTION, CONTROL, AND DECISION-MAKING OF EMBODIED INTELLIGENT SYSTEMS • Previous Articles Next Articles

Re-entry gliding vehicle trajectory prediction based on maneuver detection

Yudong HU(), Maofeng PANG(), Qingfeng DU(), Changsheng GAO()

School of Astronautics, Harbin Institute of Technology, Harbin 150001, China

Received:2025-11-12 Online:2026-02-18 Published:2026-03-09
Contact: Yudong HU E-mail:huyudong@hit.edu.cn;24s118196@stu.hit.edu.cn;duqingfeng0208@163.com;gaocs@hit.edu.cn
About author:
HU Yudong was born in 1991. He received his M.S. degree in motion control and navigation systems from Samara State Aerospace University, Russia, in 2018, and Ph.D. degree in aerospace science and technology from Harbin Institute of Technology, China, in 2022. He is currently an assistant professor with Harbin Institute of Technology. His research interests include aircraft trajectory planning, target tracking, and trajectory prediction. E-mail: huyudong@hit.edu.cn

PANG Maofeng was born in 2002. He received his B.S. degree in aerospace engineering from Central South University, Changsha, China, in 2024. He is currently pursuing his M.S. degree in mechanical engineering (aerospace engineering) with the School of Astronautics, Harbin Institute of Technology, China. His research interests include target tracking and trajectory prediction. E-mail: 24s118196@stu.hit.edu.cn

DU Qingfeng was born in 1998. He received his M.S. degree in aerospace engineering from Harbin Institute of Technology, China, in 2021, and Ph.D. degree in aerospace science and technology from the School of Astronautics, Harbin Institute of Technology. His research interests include moving-mass control and pursuit-evasion game. E-mail: duqingfeng0208@163.com

GAO Changsheng was born in 1978. He received his M.S. and Ph.D. degrees in flight vehicle design from Harbin Institute of Technology, China, in 2002 and 2007, respectively. He is currently a professor and a Ph.D. supervisor with Harbin Institute of Technology. His research interests include guidance, navigation, control of aircraft, and moving-mass control. E-mail: gaocs@hit.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (12302056) and the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (GZC20233445).

Abstract

Abstract:

Re-entry gliding vehicles exhibit high maneuverability, making trajectory prediction a key factor in the effectiveness of defense systems. To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations, a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling. Characteristic parameters are extracted from tracking data for parameterized modeling, enabling real-time identification of maneuver modes. In addition, a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data. Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations, significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.

Key words: trajectory prediction, re-entry gliding vehicle, maneuver mode identification, maneuver detection

Yudong HU, Maofeng PANG, Qingfeng DU, Changsheng GAO. Re-entry gliding vehicle trajectory prediction based on maneuver detection[J]. Journal of Systems Engineering and Electronics, 2026, 37(1): 9-17.

Figures/Tables 7

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Prediction horizon	Time/s
Prediction horizon	50	100	150	200
Longitudinal trajectory error	0.932	2.360	3.165	2.917
Ground track error	1.953	10.181	27.201	56.173
Overall error	2.916	9.729	26.026	54.318

Prediction horizon	Time/s
Prediction horizon	50	100	150	200
Longitudinal trajectory error	0.393	0.6749	0.877	1.232
Ground track error	1.176	3.755	9.556	20.151
Overall error	1.318	3.902	9.663	20.305

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[2]	Zhifei XI, An XU, Yingxin KOU, Zhanwu LI, Aiwu YANG. Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm [J]. Journal of Systems Engineering and Electronics, 2021, 32(2): 498-516.
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Re-entry gliding vehicle trajectory prediction based on maneuver detection

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