Reentry glide vehicle intent inference method via multidimensional intention fusion

doi:10.23919/JSEE.2026.000096

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (2): 712-724.doi: 10.23919/JSEE.2026.000096

• CONTROL THEORY AND APPLICATION • Previous Articles

Reentry glide vehicle intent inference method via multidimensional intention fusion

Yangchao HE(), Jiong LI(), Lei SHAO(), Chijun ZHOU(), Humin LEI()

Air Defence and Antimissile School, Air Force Engineering University, Xi’an 710038, China

Received:2024-12-16 Online:2026-04-18 Published:2026-04-30
Contact: Lei SHAO E-mail:heyangchao0913@163.com;gracefuloo1@126.com;zj_shaolei_2021@163.com;zhouchijun666@126.com;hmleinet@21cn.com
About author:
HE Yangchao was born in 1999. He received his B.S. degree from Air Force Engineering University in 2021. He is now a doctoral candidate in control science and engineering at Graduate School, Air Force Engineering University, Xi’an, China. His research interests include trajectory generation, tracking and prediction algorithms of reentry glide vehicle. E-mail: heyangchao0913@163.com

LI Jiong was born in 1979. He received his M.S. and Ph.D. degrees from Air Force Engineering University. He is now an associate professor and a supervisor for doctors at the Air and Missile Defense College, Air Force Engineering University, Xi’an, China. His research interests include the advanced guidance law design, hypersonic interception, and missile automatic controller design. E-mail: gracefuloo1@126.com

SHAO Lei was born in 1982. He received his M.S. and Ph.D. degrees from Air Force Engineering University. He is now an associate professor with the Air and Missile Defense College, Air Force Engineering University, Xi’an, China. His current research interests lie in the field of advanced guidance law design, hypersonic vehicle controller design, and hypersonic interception strategy. E-mail: zj_shaolei_2021@163.com

ZHOU Chijun was born in 1988. He received his M.S. and Ph.D. degrees from Air Force Engineering University. He is now an associate professor with the Air and Missile Defense College, Air Force Engineering University, Xi’an, China. His current research interests lie in the field of advanced guidance law design, hypersonic vehicle penetration, and trajectory prediction. E-mail: zhouchijun666@126.com

LEI Humin was born in 1960. He received his M.S. and Ph.D. degrees from Northwestern Polytechnical University, in 1989 and 1999. He is now a professor and a supervisor for doctors with the Air and Missile Defense College, Air Force Engineering University, Xi’an, China. His current research interests lie in the field of advanced guidance law design, hypersonic vehicle controller design, and hypersonic interception strategy. E-mail: hmleinet@21cn.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62173339).

Abstract

Abstract:

To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios, this paper proposes a multidimensional intention fusion-based inference method. Firstly, the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’ theorem. Secondly, the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions, and the corresponding likelihood probability is obtained by quantifying the cost of different intentions, which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving no-fly zones. Finally, a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints. The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods.

Key words: reentry glide vehicle, intent inference, intention cost function, multidimensional intention fusion model, Bayes’ theorem

Yangchao HE, Jiong LI, Lei SHAO, Chijun ZHOU, Humin LEI. Reentry glide vehicle intent inference method via multidimensional intention fusion[J]. Journal of Systems Engineering and Electronics, 2026, 37(2): 712-724.

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References 40

1	LU P Entry guidance: a unified method. Journal of Guidance, Control, and Dynamics, 2014, 37 (3): 713- 728. doi: 10.2514/1.62605
2	MA S D, YANG Y X, YANG H, et al Trajectory optimization of hypersonic vehicle considering the quasi-static assumption of pitch motion. Aerospace Science and Technology, 2024, 146, 108969. doi: 10.1016/j.ast.2024.108969
3	MA Y J, JIANG B, REN H Minimum eigenvalue based adaptive fault compensation for hypersonic vehicles. Journal of Systems Engineering and Electronics, 2023, 34 (2): 492- 500. doi: 10.23919/JSEE.2023.000039
4	LUO C X, ZHOU C J, BU X W Multi-missile phased cooperative interception strategy for high-speed and highly maneuverable targets. IEEE Trans. on Aerospace and Electronic Systems, 2024, 61 (2): 1971- 1996.
5	YU Y J, YUE C F, LI H Y, et al Modified filter for mean elements estimation with state jumping. Journal of Systems Engineering and Electronics, 2024, 35 (4): 999- 1012. doi: 10.23919/JSEE.2024.000081
6	LIU S S, YAN B B, ZHANG T, et al Three-dimensional coverage-based cooperative guidance law with overload constraints to intercept a hypersonic vehicle. Aerospace Science and Technology, 2022, 130, 107908. doi: 10.1016/j.ast.2022.107908
7	LIU B B, ADELI E, CAO Z, et al Spatiotemporal relationship reasoning for pedestrian intent prediction. IEEE Robotics and Automation Letters, 2020, 5 (2): 3485- 3492. doi: 10.1109/LRA.2020.2976305
8	BRONARS M, CHENG S, XU D F Legibility diffuser: offline imitation for intent expressive motion. IEEE Robotics and Automation Letters, 2024, 9 (11): 10161- 10168. doi: 10.1109/LRA.2024.3421849
9	ZHAO S Z, LI H P, KE Q H, et al Action-ViT: pedestrian intent prediction in traffic scenes. IEEE Signal Processing Letters, 2022, 29, 324- 328. doi: 10.1109/LSP.2021.3134194
10	BARUAH M, BANERJEE B, NAGAR A K Intent prediction in human-human interactions. IEEE Trans. on Human-Machine Systems, 2023, 53 (2): 458- 463. doi: 10.1109/THMS.2023.3239648
11	WANG L L, LI Q, LAM J, et al Intent inference in shared-control teleoperation system in consideration of user behavior. Complex & Intelligent Systems, 2021, 8 (4): 2971- 2981.
12	PERRUSQUÍA A, GUO W S. Reservoir computing for drone trajectory intent prediction: a physics informed approach. IEEE Trans. on Cybernetics, 2024, 54(9): 4939−4948.
13	WANG Y H, WANG J, WANG Y N, et al Trajectory prediction for an incoming missile via intent recognition and guidance parameter identification. Proc. of the Aerospace Frontiers Conference, 2024, 13218, 134- 143.
14	XU J J, DONG C H, CHENG L Deep neural network-based footprint prediction and attack intention inference of hypersonic glide vehicles. Mathematics, 2022, 11 (1): 185- 208. doi: 10.3390/math11010185
15	LI M J, ZHOU C J, SHAO L, et al Intelligent trajectory prediction algorithm for reentry glide target based on intention inference. Applied Sciences, 2022, 12 (21): 10796- 10820. doi: 10.3390/app122110796
16	LUO S Q, CHENG M, DING R Q, et al Human-robot shared control based on locally weighted intent prediction for a teleoperated hydraulic manipulator system. IEEE/ASME Trans. on Mechatronics, 2022, 27 (6): 4462- 4474. doi: 10.1109/TMECH.2022.3157852
17	AHMAD B I, MURPHY J K, LANGDON P M, et al Intent inference for hand pointing gesture-based interactions in vehicles. IEEE Trans. on Cybernetics, 2016, 46 (4): 878- 889. doi: 10.1109/TCYB.2015.2417053
18	AHMAD B I, MURPHY J K, LANGDON P M, et al Bayesian intent prediction in object tracking using bridging distributions. IEEE Trans. on Cybernetics, 2018, 48 (1): 215- 227. doi: 10.1109/TCYB.2016.2629025
19	FANASWALA M, KRISHNAMURTHY V Detection of anomalous trajectory patterns in target tracking via stochastic context-free grammars and reciprocal process models. IEEE Journal of Selected Topics in Signal Processing, 2013, 7 (1): 76- 90. doi: 10.1109/JSTSP.2012.2233459
20	JIA C F, MA J, DE VRIES B, et al. Bayesian inference of collision avoidance intent during ship encounters. https://ieeexplore.ieee.org/document/10488851.
21	GAN R Z, AHMAD B I, GODSILL S J Lévy state-space models for tracking and intent prediction of highly maneuverable objects. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (4): 2021- 2038. doi: 10.1109/TAES.2021.3088430
22	ZHANG K, XIONG J J, LI F, et al Bayesian trajectory prediction for a hypersonic gliding reentry vehicle based on intent inference. Journal of Astronautics, 2018, 39 (11): 1258- 1265.
23	YEPES J L, HWANG I, ROTEA M New algorithms for aircraft intent inference and trajectory prediction. Journal of Guidance, Control, and Dynamics, 2007, 30 (2): 370- 382. doi: 10.2514/1.26750
24	BEST G, FITCH R. Bayesian intention inference for trajectory prediction with an unknown goal destination. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems Congress Center Hamburg, 2015: 5817−5823.
25	HU Y D, GAO C S, LI J L, et al Novel trajectory prediction algorithms for hypersonic gliding vehicles based on maneuver mode on-line identification and intent inference. Measurement Science and Technology, 2021, 32 (11): 115012. doi: 10.1088/1361-6501/ac1284
26	LI J L, GUO J, TANG S J Trajectory prediction based on multi-model and multi-intent fusion for hypersonic gliding targets. Journal of Astronautics, 2024, 45 (2): 167- 180.
27	HAN Y C, WANG S Y, QUAN S M, et al A method of predicting for the trajectory of hypersonic gliding targets based on Bayesian inference. Control and Decision, 2024, 39 (11): 3736- 3744.
28	ZHOU H R A “current” statistical model and adaptive tracking algorithm for maneuvering targets. Acta Aeronautica et Astronautica Sinica, 1983, (1): 73- 86.
29	HE Y C, LI J, SHAO L, et al. Reentry target tracking algorithm based on improved “current” statistical model. Acta Aeronautica et Astronautica Sinica, 2024, 45(S1): 393−404.
30	JULIER S, JEFFREY U, DURRANT-WHYTE H F A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. on Automatic Control, 2000, 45 (3): 477- 482. doi: 10.1109/9.847726
31	LI J F, SONG S M, SHI X P Deep reinforcement learning based trajectory real-time planning for hypersonic gliding vehicles. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2024, 238 (16): 1665- 1682. doi: 10.1177/09544100241278023
32	LIU P, LIU H, CHEN T Y, et al Gaussian distribution-based control vector parameterization method for constrained hypersonic vehicle reentry trajectory optimization. Journal of Aerospace Engineering, 2023, 36 (6): 04023075. doi: 10.1061/JAEEEZ.ASENG-4711
33	SHAO L, LI M J, ZHAO J Local model based on-line trajectory adjustment for reentry gliding vehicle. Journal of Air Force Engineering University, 2023, 24 (3): 64- 72.
34	BARTUSIAK E R, HAO H, JACOBS M A, et al. A stochastic grammar approach to predict flight phases of a hypersonic glide vehicle. Proc. of the IEEE Aerospace Conference, 2022. DOI: 10.1109/aero53065.2022.9843362.
35	PERRUSQUÍA A, WEI Z K, GUO W S Trajectory intent prediction of autonomous systems using dynamic mode decomposition. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2024, 54 (12): 7897- 7908. doi: 10.1109/TSMC.2024.3462790
36	ZHAI D L, LEI H M, LI T, et al. Trajectory prediction of hypersonic vehicle based on adaptive IMM. Acta Aeronautica et Astronautica Sinica. 2016, 37(11): 3466−3475.
37	PHILLIPS T H. A common aero vehicle (CAV) model, description, and employment guide. Arlington: Schafer Corporation for AFRL and AFSPC, 2003: 1−12.
38	SHEN Z J, LU P Dynamic lateral entry guidance logic. Journal of Guidance, Control, and Dynamics, 2004, 27 (6): 949- 959. doi: 10.2514/1.8008
39	LIANG Z X, LIU S Y, LI Q D, et al Lateral entry guidance with no-fly zone constraint. Aerospace Science and Technology, 2017, 60, 39- 47. doi: 10.1016/j.ast.2016.10.025
40	XIAO X, YANG Y, GUO T, et al Discussion of two algorithms for generating landing footprint for lifting reentry vehicles. Aerospace Control, 2015, 33 (2): 39- 43.

Method	Cost characteristics
Method 1	Shortest path distance increment, heading deviation angle, dangerous radius of no-fly zone
Method 2	Remaining range, heading deviation angle, dangerous radius of no-fly zone
Method 3	Pseudo heading deviation angle, relative energy

Reentry glide vehicle intent inference method via multidimensional intention fusion

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