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。 Due to the influence of atmospheric density, aerodynamic parameters, gusts and sandstorms, Mars rover usually brings unknown input into the dynamic model of high-speed flight. These unknown inputs make the traditional filtering methods have big deviations. The study uses a new self-calibrated extended Kalman filter to estimate the state of the detector entering the segment of Mars, and successfully eliminates the influence of these unknown inputs. Numerical simulation results show that this method can effectively improve the navigation accuracy.