中文摘要：

天文导航是一种广泛应用于深空探测任务的全自主导航方法。基于状态模型和量测模型的非线性卡尔曼滤波方法在天文导航中被广泛使用.卡尔曼滤波要求状态和量测模型误差是高斯白噪声且先验协方差信息已知,但在深空探测器天文导航系统中,状态模型和量测模型噪声通常不能精确知道且是时变的.因此,自适应卡尔曼滤波器广泛用于解决状态和量测模型误差未知且时变的问题。本文首先结合火星探测器接近段的实际情况分析了火星探测器接近段模型噪声的时变特性,然后对三种常用的在线调节自适应滤波方法在火星探测接近段的滤波表现进行了仿真实验。

英文摘要：

Celestial navigation is an autonomous navigation method for deep space mission, which has been wildly researched. In celestial navigation, nonlinear Kalman filters such as Unscented Kalman Filter （UKF） are usually used. However, for traditional nonlinear Kalman filter, the system model errors should be Gaussian noise and their statistical characteristics should be known. However, the information is usually unknown and the system model errors are usually time-varying in real system. Hence, many adaptive filters have been studied to solve this problem. In this paper, three adaptive filters based on adjusting the predicted state covariance matrix are compared by simulation. Before this, the system model errors of celestial navigation are also analyzed for the approach phase.