中国空间科学技术

中国空间科学技术 ›› 2020, Vol. 40 ›› Issue (5): 53-60.doi: 10.16708/j.cnki.1000-758X.2020.0058

• 研究探讨 • 上一篇    下一篇

深空探测航天器巡航段自主导航方法研究

叶子鹏，周庆瑞，王辉   

  1. 中国空间技术研究院钱学森空间技术实验室，北京100094，中国
  • 出版日期:2020-10-25 发布日期:2020-09-30
  • 基金资助:
    国家重点研发计划（2018YFA0703800）

Research on autonomous navigation method for deep space exploration spacecraft in cruise phase

YE Zipeng，ZHOU Qingrui，WANG Hui   

  1. Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing100094, China
  • Published:2020-10-25 Online:2020-09-30

PDF (PC)

615

摘要： 对深空探测航天器自主导航方法进行了研究。为了应对深空探测中航天器轨道动力学模型的误差，在分光计测量航天器相对于太阳径向速度基础上，引入了小行星的视线矢量测量。通过最小二乘法计算出由小行星视线矢量所得到的位置信息，采用改进的信息融合方法修正扩展卡尔曼滤波中不精确的动力学模型造成的状态估计误差。同时计算了模型的能观度，对模型的可观性进行了分析。最后对算法进行了仿真分析，仿真结果表明，该算法对动力学模型的依赖性明显低于其他算法，在相同模型精度下，可获得更好的滤波精度。

关键词: 深空自主导航, 径向速度, 小行星视线矢量, 最小二乘法, 扩展卡尔曼滤波, 能观度

Abstract: A method for autonomous navigation of deep space exploration spacecraft was proposed. In order to cope with the error of the spacecraft orbital dynamics model in deep space exploration, not only the radial velocity of the spacecraft relative to the sun, but also the line of sight vector of the asteroid was measured. The position information obtained by the asteroid's line of sight vector was calculated by the least squares method, and the state estimation error caused by the inexact dynamic model in the extended Kalman filter was modified by the improved information fusion method. At the same time, the observability of the model was calculated and analyzed. The simulation results show that the dependence of the algorithm on the dynamic model is significantly lower than that of the other algorithms. Under the same model precision, better result can be obtained.

Key words: autonomous navigation for deep space exploration, radial velocity, line of sight vector of the asteroid, least squares method, extended Kalman filter, observability