基于随机有限集的空间碎片群运动状态估计

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。

State estimation of space debris group based on random finite set

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 pro-cessing used after filtering ,this filter accomplishes the estimation of object density distribution ,object number and conspicu-ous 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 .