Adaptive Extended Kalman Filtering Strategies for Autonomous Relative Navigation of Formation Flying Spacecraft
Adaptive Extended Kalman Filtering Strategies for Autonomous Relative Navigation of Formation Flying Spacecraft
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编队飞行航天器自主相对导航的自适应扩展卡尔曼滤波策略
DOI：
10.22215/etd/2018-13421
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Cory Fraser
中科院分区：
文献类型：
--
作者：
Cory Fraser
为了增强星载自主制导、导航和控制系统的能力，本文提出了两种用于航天器编队飞行的自适应扩展卡尔曼滤波导航算法的开发。所提出的自适应滤波器能够实时更新卡尔曼滤波器的内部噪声特性，并且在所有轨道场景中都是可行的，包括存在扰动的高椭圆轨道。第一种卡尔曼滤波器方法使用最大似然估计技术来导出滤波器的分析适应法则，第二种方法使用基于滤波器残差的协方差匹配分析的嵌入式模糊逻辑系统。三个航天器编队的数值模拟用于证明所提出的自适应导航算法对于过滤初始化误差、动力学建模缺陷和测量噪声比标准扩展卡尔曼滤波器明显更稳健。
To enhance the capabilities of onboard autonomous guidance, navigation and control systems, this thesis presents the development of two adaptive extended Kalman filter navigation algorithms for spacecraft formation flying. The proposed adaptive filters are capable of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit scenarios, including highly elliptical orbits in the presence of perturbations. The first Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws for the filter, and the second approach uses an embedded fuzzy logic system based on a covariance-matching analysis of the filter residuals. Numerical simulations of three spacecraft formations are used to demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter initialization errors, dynamics modelling deficiencies, and measurement noise than the standard extended Kalman filter.