摘要
为提高飞行器的在线辨识精度,有效降低制导系统的误差,以无动力滑翔飞行器为研究对象,以扩展卡尔曼滤波为基础,结合迭代滤波理论,提出了一种改进的迭代扩展卡尔曼滤波算法以实现飞行器气动参数的在线辨识。该方法在扩展卡尔曼滤波得到的估计点上,对量测方程重新进行泰勒级数展开,并进行进一步的迭代,能有效降低算法的线性化误差。在仿真实验中,将传统的卡尔曼滤波方法与该方法进行对比,结果表明,该方法具有较好的收敛性,同时提高了参数辨识的精度,并在计算时间上符合在线辨识的要求。
An improved method of on-line identification called iterative extended kalman filter algorithm is proposed to identify the aerodynamic parameters of the aircraft based on the extended kalman filter and combined with iterative filtering theory.In this method,the Taylor series expansion is carried out on the estimation point obtained by the extended kalman filter,as well as the further iteration,which can effectively reduce the linearization error of the algorithm.In the simulation experiment,the traditional kalman filter method is compared with the proposed method.
出处
《工业控制计算机》
2018年第12期13-16,共4页
Industrial Control Computer
关键词
滑翔飞行器
气动参数
在线辨识
卡尔曼滤波
gliding aircraft
aerodynamic parameters
on-line identification
kalman filter
