航天器天文导航模糊自适应卡尔曼滤波研究

张瑜; 房建成

航天器天文导航模糊自适应卡尔曼滤波研究

张瑜,
房建成

北京航空航天大学宇航学院, 北京 100083

基金项目: 国家自然科学基金资助项目(60174031); 863计划资助项目(2002AA713031)

详细信息

中图分类号: V 44822⁺4; TP 202⁺7
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出版历程
- 收稿日期: 2003-05-13
- 网络出版日期: 2004-08-31

Fuzzy adaptive Kalman filtering for spacecraft celestial navigation

School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

摘要

摘要: 星光折射间接敏感地平的自主天文导航方法能够获得很高的导航精度,但由于大气密度模型时空分辨率不高,这种方法所敏感的地平有可能出现较大的瞬时误差,从而导致导航滤波器精度降低,有时甚至发散.为了解决这一问题,提出将模糊推理系统应用于自主天文导航,研究基于Unscented卡尔曼滤波的模糊自适应算法,使导航滤波器在观测值异常时具有一定的自适应能力.计算机仿真结果验证了该方法的有效性.
- 自主式导航 /
- 自适应滤波 /
- 星光大气折射 /
- Unscented卡尔曼滤波 /
- 模糊推理系统
Abstract: High positioning precision can be obtained by autonomous navigation method based on indirectly sensing horizon by stellar atmospheric refraction. Because of the limited resolution of the atmosphere density model, large instantaneous error would occur in the horizon sensed by this method. In this case, the performance of the navigation filter would be degraded, even divergent. A fuzzy inference system was applied to autonomous celestial navigation and a fuzzy adaptive algorithm based on the Unscented Kalman filter was presented to resolve the problem above. Stronger adaptive ability of navigation filter can be obtained by this adaptive algorithm when the observation is abnormal. The computer simulation results demonstrate the validity of this method.
- autonomous navigation /
- adaptive filtering /
- stellar atmospheric refraction /
- Unscented Kalman filtering /
- fuzzy inference system

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参考文献(1)

[1] White R L,Gounley R B. Satellite autonomous navigation with SHAD . CSDL-R-1982, Cambridge:The Charles Stark Draper Laboratory,Inc,1987 [2] 杨博,房建成,伍小洁.星光折射航天器自主定轨方案比较[J].航天控制,2001,19(3):1~6,11 Yang Bo, Fang Jiancheng, Wu Xiaojie. Comparisons of autonomous orbit determination methods for spacecraft using starlight refraction [J]. Aerospace Control, 2001, 19(3):1~6,11(in Chinese) [3] Van der Merwe R, Wan E A. The square-root Unscented Kalman filter for state and parameter-estimation . IEEE International Conference on Acoustics, Speech, and Signal Processing . Piscataway:Institute of Electrical and Electronics Engineers, 2001. 3461~3464 [4] Escamilla-Ambrosio P J, Mort N. Multisensory data fusion architecture based on adaptive Kalman filter and fuzzy logic performance assessment . The Fifth International Conference on Information Fusion . Sunnyvale:International Society of Information Fusion, 2002. 1542~1549 [5] 闻新,周露,李东江,等. Matlab模糊逻辑工具箱的分析与应用[M].北京:科学出版社,2001.151~174 Wen Xin, Zhou Lu, Li Dongjiang, et al. Analysis and application of Matlab fuzzy logic tool box[M].Beijing:Science Press, 2001. 151~174(in Chinese)

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