The attitude estimation of a spacecraft in low Earth orbit is considered with the design of two different adaptation rules in the extended Kalman filter (EKF) algorithm. The adaptations are designed for compensating both the measurement faults and external disturbances by updating the noise covariances of the Kalman filter. First, the measurement noise covariance (R) adaptation is introduced by using the Singular Value Decomposition (SVD) as a preprocessing step in EKF design. The estimation filters might suffer from the large erroneous initialization of the states by diverging from the actual case. The proposed algorithm on the other hand uses SVD measurements as the initial conditions for the filtering stage. This makes the filter resistant to this type of error source. Second, the process noise covariance (Q) adaptation rule is incorporated into the previous filter design. The rules are set simultaneously so that the filter has the capability to be robust against initialization errors, system noise uncertainties, and measurement malfunctions. Numerical simulations based on several scenarios are employed to investigate the robustness of the filter.

在扩展卡尔曼滤波器（EKF）算法中设计了两种不同的自适应规则，考虑了近地轨道航天器的姿态估计。这些调整旨在通过更新卡尔曼滤波器的噪声协方差来补偿测量故障和外部干扰。首先，通过使用奇异值分解 (SVD) 作为 EKF 设计中的预处理步骤，引入了测量噪声协方差 (R) 自适应。由于偏离实际情况，估计滤波器可能会遭受状态的大错误初始化。另一方面，所提出的算法使用 SVD 测量作为滤波阶段的初始条件。这使得过滤器能够抵抗这种类型的错误源。第二，过程噪声协方差 (Q) 自适应规则被纳入到先前的滤波器设计中。这些规则是同时设置的，因此滤波器能够对初始化错误、系统噪声不确定性和测量故障具有鲁棒性。基于几种情况的数值模拟被用来研究滤波器的鲁棒性。