航空学报 > 2017, Vol. 38 Issue (11): 321200-321200   doi: 10.7527/S1000-6893.2017.321200

基于随机有限集的空间碎片群运动状态估计

卢哲俊, 胡卫东   

  1. 国防科学技术大学 电子科学与工程学院 ATR国家重点实验室, 长沙 410073
  • 收稿日期:2017-02-28 修回日期:2017-04-28 出版日期:2017-11-15 发布日期:2017-04-28
  • 通讯作者: 胡卫东 E-mail:wdhu@nudt.edu.cn
  • 基金资助:

    国家自然科学基金(61372162)

State estimation of space debris group based on random finite set

LU Zhejun, HU Weidong   

  1. ATR Key Lab, College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2017-02-28 Revised:2017-04-28 Online:2017-11-15 Published:2017-04-28
  • Supported by:

    National Natural Science Foundation of China(61372162)

摘要:

传统的空间目标监测是建立在单目标状态估计基础之上,在面对突发产生的大量空间碎片时,由于碎片尺寸小,且密集分布以"群"的方式出现,传统单目标处理方法很难奏效。以"群"整体作为处理对象,基于随机有限集(RFS)技术,对"群"的状态特征进行估计。为了解决漏检目标密度分配问题和轨迹关联问题,提出一种面向量测的改进集势概率假设密度(CPHD)滤波器,并结合滤波后的信息处理过程,完成了对低轨空间碎片群的目标密度分布、群内目标数以及群内显著目标的状态估计。在仿真实验中,提出的滤波器表现明显优于传统滤波器和标准CPHD滤波器,且在某些传统滤波器和标准CPHD滤波器已失效的情况下,所提技术仍能有效工作。

关键词: 空间碎片, 群目标, 状态估计, 随机有限集, 改进CPHD滤波器

Abstract:

Based on the single target state estimation,the conventional approach is not able to work well when faced with a large number of suddenly generated space debris objects,as those objects are closely-spaced as a group with small size.Thus,based on the Random Finite Set (RFS) theory,the space debris group is treated as the processing object and its states are estimated in this work.In order to address the issues of missed object density distribution and trajectory association,an improved measurement-oriented Cardinalized Probability Hypothesis Density (CPHD) filter is proposed.With a data processing used after filtering,this filter accomplishes the estimation of object density distribution,object number and conspicuous object state in a group.In simulations,the proposed filter significantly outperforms the conventional filter and CPHD filter.It can work in challenging environment,and meanwhile,the conventional filter and CPHD filter fail.

Key words: space debris, group object, state estimation, random finite set, improved CPHD filter

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