HGV状态和参数估计的动力学模型

doi:10.12305/j.issn.1001-506X.2026.09.28

摘要/Abstract

摘要：

在高超声速滑翔飞行器（hypersonic gliding vehicle，HGV）跟踪问题中，动力学模型起到了关键作用。然而，由于再入过程中目标的结构时变以及流场等不确定因素的作用，导致目标的运动模式时变且未知。基于此，提出了以扩维半弹道再入飞行器（semi-ballistic reentry vehicle，SBRV）模型估计目标的状态和气动参数。基于目标机动过程中升阻比有界以及气动参数耦合的特性，采用SBRV模型估计的爬阻比、转阻比具有较快的收敛速度，且阻力参数的估计误差也保持总体最小。进一步分析了该模型与两种典型动力学模型之间的转换关系。通过对HGV典型机动场景的仿真跟踪表明，基于SBRV模型设计的估计器跟踪误差更小，且对目标机动检测的延时更小。

关键词: 高超声速滑翔飞行器, 机动目标跟踪, 半弹道再入飞行器, 状态估计, 参数估计

Abstract:

In the study of hypersonic gliding vehicle （HGV） tracking problems, dynamic models play a crucial role. However, due to the effects of uncertainties such as the time-variant structure of the target and the flow field during the reentry process, the motion modes of the target are time-variant and unknown. Based on this, the semi-ballistic reentry vehicle （SBRV） model with state augmentation is proposed to estimate the state and aerodynamic parameters of the target. By considering the bounded characteristics of the lift-to-drag ratio and the coupling of aerodynamic parameters during the target’s maneuvering process, the SBRV model provides rapid convergence in estimating the climb-to-drag ratio and the turn-to-drag ratio, while maintaining globally minimized estimation errors in the drag parameter. The conversion relationship between this model and two typical dynamic models is further analyzed. Through the tracking simulation in the typical maneuvering scenarios of HGV, it is demonstrated that the estimator based on the SBRV model has a smaller tracking error and a smaller delay for target maneuver detection.

Key words: hypersonic gliding vehicle （HGV）, maneuvering target tracking, semi-ballistic reentry vehicle （SBRV）, state estimation, parameter estimation

中图分类号:

V 557+.5

刘豪, 梁勇奇. HGV状态和参数估计的动力学模型[J]. 系统工程与电子技术, 2026, 48(9): 3159-3166.

Hao Liu, Yongqi Liang. Dynamic model for state and parameter estimation of HGV[J]. Systems Engineering and Electronics, 2026, 48(9): 3159-3166.

图/表 11

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表1

参考文献 33

1	张博伦, 周荻, 吴世凯. 临近空间高超声速飞行器机动模型及弹道预测[J]. 系统工程与电子技术, 2019, 41 (9): 2072.
2	朱金钊, 周荻, 陈晓波, 等. 临近空间高超声速飞行器快时变机动模型及轨迹预报[J]. 系统工程与电子技术, 2025, 47 (1): 244.
3	赵良玉, 雍恩米, 王波兰. 反临近空间高超声速飞行器若干研究进展[J]. 宇航学报, 2020, 41 (10): 1239.
4	Chang C B, Athans M, Whiting R H. On the state and parameter estimation for maneuvering reentry vehicles[J]. IEEE Trans. on Automatic Control, 1977, 22 (1): 99. doi: 10.1109/TAC.1977.1101412
5	Li Z, Wang Y D, Zheng W. Adaptively tracking hypersonic gliding vehicles[J]. Aerospace Science and Technology, 2024, 147, 109035. doi: 10.1016/j.ast.2024.109035
6	Mallick M, Tian X Q, Xu L F. Reentry vehicle filtering using a passive angle-only sensor[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (6): 8539. doi: 10.1109/TAES.2024.3432113
7	Li G W, Wang Q, Zhang S R, et al. Interacting multiple-mode estimation using centroid fixed structure for high precision[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (1): 802. doi: 10.1109/TAES.2023.3328546
8	Zhang K, Xiong J J, Fu T T, et al. Coupled dynamic model of state estimation for hypersonic glide vehicle[J]. Journal of Systems Engineering and Electronics, 2018, 29 (6): 1284. doi: 10.21629/JSEE.2018.06.15
9	Liang Y Q, Han C Z. Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle[J]. Science China Information Sciences, 2011, 54 (4): 812. doi: 10.1007/s11432-010-4159-6
10	Hough M E. Reentry maneuver estimation using nonlinear Markov acceleration models[J]. Journal of Guidance, Control, and Dynamics, 2017, 40 (7): 1693.
11	Hough M E. Acceleration characterization for reentry orbit determination with unmodeled maneuvers[J]. Journal of Guidance, Control, and Dynamics, 2018, 41 (7): 1463.
12	Liu H, Liang Y Q. A systematic multiple-model estimator for tracking hypersonic gliding vehicle[J]. Aerospace Science and Technology, 2025, 164, 110448. doi: 10.1016/j.ast.2025.110448
13	Liang Y Q, Li X R, Han C Z, et al. A general systematic method for model-set design[J]. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (2): 1505. doi: 10.1109/TAES.2012.6178075
14	Jia B, Xin M, Cheng Y. High-degree cubature Kalman filter[J]. IEEE Trans. on Automatic Control, 2013, 49 (2): 510.
15	Luan X L, Xue W, Zhao S Y, et al. A Kalman and fading memory cofilter for uncertain systems based on self-perception mechanism[J]. IEEE Trans. on Automatic Control, 2025, 70 (8): 5021. doi: 10.1109/TAC.2025.3532813
16	Wang X X, Chen H, Lian F, et al. Robust Kalman filter and smoother based on the student’s t minimum error entropy criterion[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (3): 7995. doi: 10.1109/TAES.2025.3529410
17	Han B Y. Distributionally robust Kalman filtering with volatility uncertainty[J]. IEEE Trans. on Automatic Control, 2025, 70 (6): 4000. doi: 10.1109/TAC.2024.3522192
18	Liu H Y, Sun X C, Bai S Z. Modified unscented Kalman filter considering maximum point of probability density function[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (2): 4926. doi: 10.1109/TAES.2024.3516703
19	Arasaratnam I, Haykin S. Cubature Kalman filters[J]. IEEE Trans. on Automatic Control, 2009, 54 (6): 1254. doi: 10.1109/TAC.2009.2019800
20	Arasaratnam I, Haykin S., Hurd T R Cubature Kalman filtering for continuous-discrete systems: theory and simulations[J]. IEEE Trans. on Signal Processing, 2010, 58 (10): 4977. doi: 10.1109/TSP.2010.2056923
21	Tichavsky P, Muravchik C H, Nehorai A. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering[J]. IEEE Trans. on Signal Processing, 1998, 46 (5): 1386. doi: 10.1109/78.668800
22	Sun J. Yuan Y, Wang Y, et al. Enumeration PCRLB-based power allocation for multitarget tracking with colocated MIMO radar systems in clutter[J]. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5105318.
23	Zhang C L, Shi W T, Gong Z J, et al. Target tracking using time delay and Doppler shift with nonnegligible motion effect in bistatic sonar[J]. IEEE Sensors Journal, 2023, 23 (14): 15910. doi: 10.1109/JSEN.2023.3281938
24	Xu L F, Li X R, Mallick M, et al. Modeling and state estimation of destination-constrained dynamic systems Part II: uncertain arrival time[J]. IEEE Trans. on Signal Processing, 2025, 73, 633. doi: 10.1109/TSP.2024.3454972
25	Li X R, Jilkov P V. Survey of maneuvering target tracking - Part III: measurement models[C]//Signal and Data Processing of Small Targets, 2001.
26	Li X R, Jilkov P V. Survey of maneuvering target tracking - Part II: motion models of ballistic and space targets[J]. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46 (1): 96. doi: 10.1109/TAES.2010.5417150
27	Phillips T H. A common aero vehicle (CAV) model, description, and employment guide[R]. Arlington: Schafer Corporation, 2003.
28	Shen Z J, Lu P. Onboard generation of three-dimensional constrained entry trajectories[J]. Journal of Guidance, Control, and Dynamics, 2003, 26 (2): 111.
29	Yu W B, Chen W C. High-accuracy approximate solutions for hypersonic gliding trajectory with large lateral maneuvering range[J]. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (3): 1498. doi: 10.1109/TAES.2020.3043532
30	Schwartz E, Kappenko M, Ross I M. Unscented hypersonic trajectory optimization with a heating-rate chance constraint[J]. Journal of Guidance, Control, and Dynamics, 2025, 48 (6): 1227.
31	Shachar S, Ben-Asher J Z, Diepolder J. Optimal trajectory for a hypersonic cruise missile with a nonconvex control set[J]. Journal of Guidance, Control, and Dynamics, 2025, 48 (6): 1241.
32	Li X R, Zhao Z. Evaluation of estimation algorithms - Part I: incomprehensive measures of performance[J]. IEEE Trans. on Aerospace and Electronic Systems, 2006, 42 (4): 1340. doi: 10.1109/TAES.2006.314576
33	吴楠, 陈磊. 高超声速滑翔再入飞行器弹道估计的自适应卡尔曼滤波[J]. 航空学报, 2013, 34 (8): 1960.

场景	统计指标	MaRV	Bank	SBRV
场景	统计指标	MaRV	Bank	λ_θ1	λ_θ2	λ_θ3	λ_θ4
场景1	位置/m	97.56	92.01	90.97	78.43	69.90	67.31
	速度/（m/s）	23.48	22.52	57.70	31.26	20.18	17.66
	α_d/（m²/kg）	4.28×10⁻⁶	3.76×10⁻⁶	1.76×10⁻⁵	4.50×10⁻⁶	2.19×10⁻⁶	3.65×10⁻⁶
	α_t/（m²/kg）	2.90×10⁻⁵	2.87×10⁻⁵	4.55×10⁻⁵	1.84×10⁻⁵	1.60×10⁻⁵	2.09×10⁻⁵
	α_c/（m²/kg）	7.36×10⁻⁶	9.26×10⁻⁶	7.55×10⁻⁵	1.27×10⁻⁵	6.24×10⁻⁶	6.11×10⁻⁶
场景2	位置/m	83.41	68.56	86.24	76.38	66.8	61.88
	速度/（m/s）	19.68	16.27	48.53	28.21	18.31	15.46
	α_d/（m²/kg）	3.49×10⁻⁶	3.02×10⁻⁶	2.09×10⁻⁵	4.92×10⁻⁶	2.66×10⁻⁶	4.54×10⁻⁶
	α_t/（m²/kg）	2.60×10⁻⁵	2.27×10⁻⁵	3.12×10⁻⁵	1.76×10⁻⁵	1.54×10⁻⁵	1.97×10⁻⁵
	α_c/（m²/kg）	1.23×10⁻⁵	1.43×10⁻⁵	9.56×10⁻⁵	1.66×10⁻⁵	9.16×10⁻⁶	9.54×10⁻⁶

[1]	卓德胜, 朱锋, 张小红, 程军龙, 胡捷. 多相机组合式全景视觉惯性SLAM系统[J]. 系统工程与电子技术, 2026, 48(8): 2809-2820.
[2]	刘子渤, 孙伟峰, 张鹏, 张超, 刘奇. 基于时频能量分布聚类的跳频信号时间参数估计方法[J]. 系统工程与电子技术, 2026, 48(8): 2872-2878.
[3]	周伟, 郭浩南, 侯长波, 孟昊, 万凯. 基于改进DBSCAN算法的跳频网台分选方法[J]. 系统工程与电子技术, 2026, 48(7): 2492-2500.
[4]	梁雨凡, 陈映, 李鑫, 窦凇耀. 基于ACT和Bi-LSTM的多模型交互跟踪算法[J]. 系统工程与电子技术, 2026, 48(6): 1869-1879.
[5]	王新, 周生华, 张新勋, 南静怡, 孙世坤. 全极化MIMO雷达主瓣压制干扰抑制方法[J]. 系统工程与电子技术, 2026, 48(5): 1539-1550.
[6]	王博, 周文雅, 柯肇捷, 宗飞, 刘君. 改进熵权和灰关联模型的装备能力评估[J]. 系统工程与电子技术, 2026, 48(2): 524-534.
[7]	高宁, 许枫, 杨娟. 基于FrFT的MIMO声纳水下运动小目标参数估计算法[J]. 系统工程与电子技术, 2025, 47(9): 2797-2807.
[8]	孙一心, 方愚渊, 张磊. 战术机动先验辅助的交互多模型目标跟踪方法[J]. 系统工程与电子技术, 2025, 47(7): 2086-2097.
[9]	巴晓辉, 温雯霏, 蔡伯根, 王剑, 姜维, 柴琳果. GNSS信号的线性调频干扰抑制方法[J]. 系统工程与电子技术, 2025, 47(7): 2339-2348.
[10]	张游, 黄永辉, 李志豪, 崔天舒, 王竹刚. 基于Savitzky-Golay滤波和互模糊函数的时频差参数估计算法[J]. 系统工程与电子技术, 2025, 47(5): 1395-1403.
[11]	闫咏琪, 王琪, 易凡. 交互式多模型概率数据关联抗速度拖引干扰算法[J]. 系统工程与电子技术, 2025, 47(5): 1507-1515.
[12]	徐宙, 刘连照, 周凯, 王道酉, 马晖. 基于参数估计的SMSP干扰解析对消方法[J]. 系统工程与电子技术, 2025, 47(12): 4048-4056.
[13]	孙博, 林桐疏, 吴泽豫, 钱诚, 潘俊林, 周乐阳, 董珂鸣. 基于退化注入场路耦合模型的锂电池SOC估计方法[J]. 系统工程与电子技术, 2025, 47(10): 3270-3277.
[14]	付卫红, 周雨菲, 张鑫钰, 刘乃安. 基于参数估计和Kalman滤波的单通道盲源分离算法[J]. 系统工程与电子技术, 2024, 46(8): 2850-2856.
[15]	姚思亦, 李万春, 高林, 张花国, 胡航玮. 基于分布式PMHT的多传感器多目标跟踪[J]. 系统工程与电子技术, 2024, 46(7): 2184-2190.