基于卡尔曼滤波的再入飞行器气动参数辨识

Aerodynamic parameter identification of a reentry vehicle based on Kalman filter method

再入飞行器的制导系统易受气动参数扰动的影响,为此研究气动参数在线辨识方法可以为再入制导系统提供服务,有效提高制导的精度。以卡尔曼滤波理论作为基础,推导了再入飞行器气动参数辨识的数学模型。为增强卡尔曼滤波方法对气动参数的辨识效果,气动参数误差模型采用一阶高斯马尔科夫过程描述,并增广到状态方程组中,根据获得的带有测量误差的惯导信息,对气动参数进行估计。最后,进行了数学仿真研究。仿真结果表明,该方法都能够在10个采样周期内收敛,且估计精度在1%以内。

The guidance system of a reentry vehicle is vulnerable to the perturbation of the aerodynamic parameter. In this paper, an online identification method for aerodynamic parameter is studied to provide services for the guidance system and improve the guidance precision. The mathematical model of aerodynamic parameter identification is deduced based on Kalman filter theory. In order to improve the effect, an error model of aerodynamic parameter is described by using a first-order Gauss-Markov process and is augmented into the state equations. Based on these, a Kalman filter is used to estimate the aerodynamic parameters by using the obtained inertial navigation information with measurement errors. Finally, the simulations are performed, which show that the proposed method can realize convergence within 10 sampling period, and the estimate error is within 1%. ©, 2014, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.