基于自适应EKF的空天飞行器再入段大气参数估计方法
Estimation method of air data based on adaptive extended Kalman filter in reentry phase of aerospace vehicle
李荣冰 1王宇 1程鉴皓 1阚梦怡1
作者信息
- 1. 南京航空航天大学 导航研究中心,南京 211106
- 折叠
摘要
针对空天飞行器再入段计算虚拟大气参数时,风速状态系统噪声和导航系统量测噪声具有时变特性导致滤波精度下降问题,提出一种基于自适应EKF的大气参数估计方法.首先,基于气动模型下的动力学方程组,建立风速和大气参数估计模型;其次采用Sage-Husa自适应滤波方法对风速状态系统噪声和导航量测噪声进行自适应调整,并且针对可能存在的滤波发散问题,引入滤波发散判据,调整滤波过程参数,提高了滤波稳定性.最后进行了仿真验证,仿真结果表明,所提出的自适应EKF滤波方法具有较好的风速和大气参数估计精度和收敛稳定性,其中在风向、真空速和侧滑角估计精度上较EKF和传统Sage-Husa滤波算法均提高了 20%以上.
Abstract
In order to solve the problem that the filtering accuracy is reduced due to the time-varying characteristics of wind velocity state system noise and navigation system measurement noise during the calculation of virtual air parameters of space vehicle reentry stage,an adaptive EKF based air parameter estimation method is proposed.Firstly,based on the dynamic equations under the aerodynamic model,the estimation model of wind speed and air parameters is established.Secondly,the Sage-Husa adaptive filtering method is used to adjust the noise of wind speed state system and navigation measurement noise.In order to solve the possible filtering divergence problem,the filtering divergence criterion is introduced and the filtering process parameters are adjusted to improve the filtering stability.Finally,the simulation results show that the proposed adaptive EKF filtering method has better estimation accuracy and convergence stability of wind speed and air parameters,and the estimation accuracy of wind direction,true airspeed and side-slip angle is improved by more than 20%compared with the traditional EKF and Sage-Husa filtering algorithms.
关键词
空天飞行器/虚拟大气参数/Sage-Husa自适应滤波/扩展卡尔曼滤波Key words
aerospace vehicle/virtual air parameter/Sage-Husa adaptive filtering/extended Kalman filter引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据