月球探测器天文导航的遗传粒子滤波方法

房建成; 宁晓琳

月球探测器天文导航的遗传粒子滤波方法

北京航空航天大学仪器科学与光电工程学院, 北京 100083

基金项目: 国家自然科学基金资助项目(60574086);新世纪优秀人才支持计划资助项目(NCET-04-0162)

详细信息

作者简介:
房建成(1965-),男,山东临沂人,教授,fangjiancheng@buaa.edu.cn.

中图分类号: V 448.2
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- 文章访问数: 2957
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- 被引次数: 0
出版历程
- 收稿日期: 2006-04-30
- 网络出版日期: 2006-11-30

Autonomous celestial navigation for lunar explorer based on genetic algorithm particle filter

School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

摘要

摘要: 天文导航系统是典型的非线性和噪声非高斯分布的系统.针对传统的扩展卡尔曼滤波不适于非线性和噪声非高斯分布的系统,和一般粒子滤波存在的粒子退化和采样枯竭问题,提出了一种基于遗传算法进行再采样的月球探测器自主天文导航粒子滤波新方法.计算机仿真结果显示了该方法可以有效的克服传统粒子滤波方法的缺点,提高天文导航系统的定位精度.
- 月球探测 /
- 自主天文导航 /
- 粒子滤波 /
- 遗传算法 /
- 扩展卡尔曼滤波
Abstract: Autonomous celestial navigation system is a typical nonlinear, non-Gaussian dynamic system. Extended Kalman filter (EKF) is widely used in spacecraft navigation. It only uses the first order terms in the Taylor series expansion. To nonlinear and non-Gaussian system, EKF may introduce large estimation error. Particle filter(PF) is a computer-based method for implementing a recursive Bayesian filter by Monte Carlo simulations. PF is an effective solution at dealing with nonlinear and/or non-Gaussian problems. The performance of PF relies on the choice of importance sampling density and resampling scheme. To overcome the particle degeneration and sample impoverishment problems existing in traditional particle filter method, a new autonomous celestial navigation method for lunar explorer based on genetic algorithm particle filter method is presented. Simulation results demonstrat the validity and feasibility of this new method.
- lunar exploration /
- autonomous celestial navigation /
- particle filter /
- genetic algorithm /
- extended Kalman filter

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参考文献(1)

[1] Kinman Peter W. Doppler tracking of planetary spacecraft[J]. IEEE Transactions on Microwave Theory and Techniques (ISSN 0018-9480), 1992, 40(6):1199-1204 [2] Asai Y, Nishimura T. Orbit determination by means of Kalman filter using spl Delta VLBI data Proceedings of the 1999 IEEE International Conference on Control Applications. Kohala Coast-Island of Hawai′i, USA:Institute of Electrical and Electronic Engineers Inc,1999:968-972 [3] Arulampalam S, Maskell S, Gordan N, et al. A tutorial on particle filters for online nonlinear/nongaussian bayesian tracking[J]. IEEE Transaction on Signal Processing,2002, 50(2):174-188 [4] 宁晓琳, 房建成. 一种基于信息融合的卫星自主天文导航新方法[J].宇航学报,2003,24(6):579-583 Ning Xiaolin, Fang Jiancheng. A new method of autonomous celestial navigation for spacecraft based on information fusion[J]. Journal of Astronautics, 2003,24(6):579-583(in Chinese) [5] Fang Jiancheng, Ning Xiaolin. New autonomous celestial navigation method for lunar satellite[J]. Harbin Institute of Technology, 2003, 10(3):308-310 [6] 周文艳, 杨维廉. 月球探测器转移轨道的中途修正[J].宇航学报,2004,25(1):89-92 Zhou Wenyan, Yang Weilian. Mid-correction of trans-lunar trajectory of lunar explorer[J].Journal of Astronautics,2004,25(1):89-92(in Chinese) [7] Bashi A S, Jilkov V P, Li X R, et al. Distributed implementations of particle filters Proceedings of the Sixth International Conference Information Fusion. Cairns, Australia:Australia by Kwik Kopy North Melbourne,2003:1164-1171 [8] 张志星, 孙春在, 水谷英二. 神经-模糊和软计算[M]. 西安:西安交通大学出版社,2000:122-126 Zhang Zhixing, Sun Chunzai, Shuigu Yinger. Neuro-fuzzy and soft computing .Xi′an:Xi′an Jiaotong University Press,2000:122-126(in Chinese)

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