Issue |
JNWPU
Volume 37, Number 6, December 2019
|
|
---|---|---|
Page(s) | 1102 - 1110 | |
DOI | https://doi.org/10.1051/jnwpu/20193761102 | |
Published online | 11 February 2020 |
Trajectory Estimation of Hypersonic Glide Vehicle Based on Analysis of Aerodynamic Performance
基于气动性能分析的高超声速滑翔飞行器轨迹估计
1
School of Astronautics, Northwestern Ploytechnical University, Xi'an 710072, China
2
Shaanxi Key Laboratory of Aerospace Flight Vehicle Technology, Xi'an 710072, China
Received:
15
November
2018
Due to high speed and high maneuverability of hypersonic glide vehicles (HGVs), the state estimation of such targets has always been a research hotspot. In order to improve accuracy of the trajectory estimation, a nonlinear aerodynamic parameter model for target estimation based on aerodynamic performance analysis is proposed. Firstly, the dynamic characteristics of the hypersonic glide vehicle during the hypersonic gliding stage was analyzed. Then, aiming at HTV-2-liked vehicle, the engineering calculation method was used to form the reference aerodynamic model for the target estimation. Secondly, a deviation model described by first-order Markov process was introduced to compensate the uncertainties of the unknown maneuver information from the target. Finally, extended Kalman filter was utilized to estimate the state of the target. The simulation results show that the proposed model is able to improve the accuracy of acceleration estimation for the HTV-2-liked hypersonic gliding vehicles.
摘要
由于高超声速滑翔飞行器(HGVs)具有高速、高机动的特点,对此类目标的状态估计一直是一个研究热点。鉴于采用传统的运动学和动力学模型进行轨迹估计时加速度估计精度不高,提出一种基于气动性能分析的非线性气动参数目标估计模型。首先,在研究了高超声速滑翔飞行器跳跃再入段运动特性的基础上,以类HTV-2飞行器为研究目标,采用气动力估算方法对高超声速滑翔目标再入段的气动力进行计算分析。其次,建立与马赫数线性相关的气动参数"基准模型"和以一阶马尔可夫过程描述的气动参数"偏差模型",对目标气动参数进行非线性估计。数学仿真结果表明,该方法可以一定程度上提高卡尔曼滤波方法对该类目标的加速度估计精度。
Key words: hypersonic gliding aircraft / maneuvering target estimation / aerodynamic performance / model for aerodynamic parameter
关键字 : 高超声速滑翔飞行器 / 机动目标估计 / 气动性能 / 气动参数模型
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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