火星进入段探测器自校准状态估计

Self-Calibration Estimation of Mars Vehicle in its Entry Phase

由于大气密度、气动参数、突风和沙尘暴等因素的影响,火星探测器在进入段高速飞行的动力学模型中往往带来未知输入,这些未知输入使传统的滤波方法出现较大的偏差。研究采用一种新的自校准扩展Kalman滤波方法,对火星进入段的探测器进行状态估计,可以成功地消除这些未知输入带来的影响。数值仿真结果表明,该方法能有效提高导航精度。

There always are unknown inputs in the dynamic model of high-speed Mars vehicle during the Mars entry phase because of the atmospheric density,aerodynamic parameters,gust and sand storm,etc.Due to the effect of the unknown inputs,the traditional filtering methods may produce greater errors.This paper estimates the states of the Mars entry vehicle by using a new self-calibration extended Kalman filter,and successfully eliminates the effect of the unknown inputs.Numerical simulation shows that this self-calibration method may effectively improve navigation accuracy.