Performance Evaluation and Application of particle Filter in Autonomous Celestial Navigation System

Abstract

In order to evaluate the performance of the deep space celestial autonomous navigation system, the utility function model is proposed. This paper adopts MATLAB as a simulation platform, deep space exploration as a simulation background, Earth-Mars transfer orbit as a model, and attempts to propose an effective assessment method in Celestial Autonomous Navigation. At the same time, several common non-linear filtering algorithms will be applied in this model, with the way of numerical and graphical display to show the navigation performance effectiveness of different filtering algorithm. The results show that the evaluation method can reflect and evaluate effectively the performance of different filtering algorithm.

Keywords

evaluation method / particle filter / deep space exploration / autonomous navigation

References

[1] 吴伟仁,王大轶,宁晓琳. 深空探测器自主导航原理与技术[M]. 北京:中国宇航出版社,2011,5:1-2. Wu W R,Wang D Y,Ning X L. Principle and technology of autonomous navigation for deep space probe[M]. Beijing:China Astronautic Publishing House,2011,5:1-2.
[2] 李作虎. 卫星导航系统性能监测及评估方法研究[D]. 兰州:解放军信息工程大学,2012. Li Z H. Research on monitoring and assessment of satellite navigation system performance[D]. Lanzhou:PLA Information Engineering University,2012.
[3] 胡志刚. 北斗卫星导航系统性能评估理论与试验验证[D]. 武汉:武汉大学,2013. Hu Z G. BeiDou navigation satellite system performance assessment theory and experimental verification[D]. Wuhan:Wuhan University,2013.
[4] Huber P. Robust estimation of a location parameter[J]. Annals of Mathematical Statistics,1964,35(2):73-101.
[5] Bube K,Nemeth T. Fast line searches for the robust solution of linear systems in the hybrid L1/L2 and Huber norms[J]. Geophysics,2007,72(2):A13-A17.
[6] Handschin J E,Mayne D Q. Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering[J]. International Journal of Control,1969,9(5):547-559.
[7] 房建成,宁晓琳. 深空探测器自主天文导航方法[M]. 西安:西北工业大学出版社,2010. Fang J C,Ning X L. Autonomous celestial navigation method for deep space probe[M]. Xi'an:Northwestern Polytechnical University Press,2010.
[8] 朱志宇. 粒子滤波算法及其应用[M]. 北京:科学出版社,2010. Zhu Z Y. Particle filter algorithm and its application[M]. Beijing:Science Press Ltd.,2010.
[9] Crisan D,Doucet A. Convergence of sequential Monte Carlo methods[D]. Univ. Cambridge,2000.
[10] Doucet A,de Freitas J,Gordon N. Sequential Monto Carlo methods in practice[M]. New York:Springer,2001.
[11] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering,1960,82(1):35-45.
[12] 张瑜,房建成. 基于Unscented卡尔曼滤波器的卫星自主天文导航研究[J]. 宇航学报,2003,24(6):646-650. Zhang Y,Fang J C. Study of the satellite autonomous celestial navigation based on the unscented Kalman filter[J]. Journal of Astronautics,2003,24(6):646-650.
[13] Saaty T L. A scaling method for priorities in hierarchical structures[J]. Journal of Mathematical Psychology,1978,1(1):57-68.