摘要
为提高接近段自主导航系统的估计精度,引入基于星光角距和星光仰角的联合观测模型进行组合导航。针对深空探测任务对探测器实时性要求高,星载计算机计算能力有限的问题,用UD分解改进信息融合滤波算法,避免高维运算和大的空间存储,减少运算负担。利用状态方程的一阶泰勒展开式分析滤波周期对状态方程可观性的影响。计算机仿真证实了改进算法的可行性。
To improve the estimation accuracy of the approach stage autonomous navigation system,a combined observation model of starlight angle and star elevation angle is used in integrated navigation.Because real-time performance of probe is regarded as a main factor on deep space exploration,and data processing capability of on-board computer is limited,in the paper UD factorization is employed to improve information fusion filter algorithm,and the high-dimension computation and the large memory are avoided.Also the computational burden is reduced.In order to analyze the impact of filter cycle to the observability,the first order Taylor expansion of state equation is introduced.Simulation results verify the feasibility of the proposed algorithm.
出处
《青岛科技大学学报（自然科学版）》
CAS
2010年第4期422-427,共6页
Journal of Qingdao University of Science and Technology:Natural Science Edition
关键词
深空探测
信息融合
UD分解
组合导航
deep space exploration
information fusion
UD factorization
integrated navigation
