摘要
航天器姿态确定的模型具有严重的非线性性。而采样卡尔曼滤波(UKF)通过采用一组确定性采样得到的Sigma点比扩展卡尔曼滤波(EKF)能够更准确地近似初始分布,使滤波在不准确的初始条件下更快地收敛。利用修正罗德里格参数(MRPs)表示姿态,用动力学方程进行角速率的传播,利用UKF的改进算法迭代采样卡尔曼滤波(IUKF)对航天器的姿态进行估计。在分析IUKF性能的基础上进一步对IUKF算法作了改进,通过仿真算例将3种方法进行了比较。结果表明:IUKF及改进IUKF算法姿态参数的滤波精度比UKF更高,同时改进IUKF算法比IUKF的滤波能更快趋于稳定。
The model for spacecraft attitude determination is severely nonlinearized. The Unscented Kalman Filter uses a determinately selected set of sample points to more accurately map the probability distribution than the linearization of the standard Extended Kalman Filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. MRPs and dynamic equation were used for attitude representation and the propagation of angular velocity respectively, Iterated Unscented Kalman Filter（IUKF） and improved IUKF algorithm were used for spacecraft attitude estimation, and comparisons were maed by a simulation experience. The results indicated that IUKF and improved IUKF possessed higher accuracy than the Unscented Kalman Filter, and improved IUKF became steady earlier than IUKF.
出处
《测绘科学技术学报》
北大核心
2009年第4期250-253,共4页
Journal of Geomatics Science and Technology
基金
国家自然科学基金(40474007)
信息工程大学测绘学院研究生创新创优基金资助
关键词
采样卡尔曼滤波
迭代采样卡尔曼滤波
修正罗德里格参数
航天器
姿态确定
UKF（Unscented Kalman Filter）
IUKF（Iterated Unscented Kalman Filter）
MRPs（Modified Rodrigues Parameters）
spacecraft
attitude determination
