收稿日期: 2017-02-28
修回日期: 2017-04-28
网络出版日期: 2017-04-28
基金资助
国家自然科学基金（61372162）
State estimation of space debris group based on random finite set
Received date: 2017-02-28
Revised date: 2017-04-28
Online published: 2017-04-28
Supported by
National Natural Science Foundation of China(61372162)
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。
卢哲俊 , 胡卫东 . 基于随机有限集的空间碎片群运动状态估计[J]. 航空学报, 2017 , 38(11) : 321200 -321200 . DOI: 10.7527/S1000-6893.2017.321200
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.
[1] 刘静, 王荣兰, 张宏博, 等. 空间碎片碰撞预警研究[J]. 空间科学学报, 2004, 24(6):462-469. LIU J, WANG R L, ZHANG H B, et al. Space debris collision prediction research[J]. Chinese Journal of Space Science, 2004, 24(6):462-469(in Chinese).
[2] 李春来, 欧阳自远, 都亨. 空间碎片与空间环境[J]. 第四纪研究, 2002, 22(6):540-551. LI C L, OUYANG Z Y, DU H. Space debris and space environment[J]. Quaternary Sciences, 2002, 22(6):540-551(in Chinese).
[3] JOHNSON N L, STANSBERY E, LIOU J C, et al. The characteristics and consequences of the break-up of the Fengyun-1C spacecraft[J]. Acta Astronautica, 2008, 63(1-4):128-135.
[4] PARDINI C, ANSELMO L. Physical properties and long-term evolution of the debris clouds produced by two catastrophic collisions in Earth orbit[J]. Advances in Space Research, 2011, 48(3):557-569.
[5] BLACKMAN S S. Multiple hypothesis tracking for multiple target tracking[J]. IEEE Aerospace & Electronic Systems Magazine, 2004, 19(1):5-18.
[6] FORTMANN T E, BAR-SHALOM Y, SCHEFFE M. Sonar tracking of multiple targets using joint probabilistic data association[J]. IEEE Journal of Oceanic Engineering, 1983,8(3):173-184.
[7] HUANG J, HU W D. MCMC-particle-based group tracking of space objects within Bayesian framework[J]. Advances in Space Research, 2014, 53(2):280-294.
[8] MAHLER R P S. Statistical multisource-multitarget information fusion[M]. Norwood, MA:Artech House, 2007:305-538.
[9] WEI B, NENER B, LIU W. Tracking of space debris via CPHD and consensus[C]//International Conference on Control, Automation and Information Sciences, 2015:436-441.
[10] JONES B A, VO B N. A labeled multi-Bernoulli filter for space object tracking[C]//AAS/AIAA Space Flight Mechanics Meeting, 2014.
[11] MAHLER R P S. Advances in statistical multisource-multitarget information fusion[M]. Norwood, MA:Artech House, 2014:181-215,379-403
[12] MAHLER R P S. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Transactions on Aerospace & Electronic Systems, 2003, 39(4):1152-1178.
[13] MAHLER R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace & Electronic Systems, 2007, 43(4):1523-1543.
[14] MAHLER R P S, ZAJIC T. Bulk multitarget tracking using a first-order multitarget moment filter[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2002, 4729:175-186.
[15] ERDINC O, WILLETT P, BAR-SHALOM Y. Probability hypothesis density filter for multitarget multisensor tracking[C]//International Conference on Information Fusion, 2005:1-8.
[16] FRANKEN D, SCHMIDT M, ULMKE M. "Spooky action at a distance" in the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Aerospace & Electronic Systems, 2009, 45(4):1657-1664.
[17] PANTA K, CLARK D E, VO B N. Data association and track management for the gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Aerospace & Electronic Systems, 2009, 45(3):1003-1016.
[18] 欧阳成, 姬红兵, 张俊根. 一种改进的CPHD多目标跟踪算法[J]. 电子与信息学报, 2010, 32(9):2112-2118. OUYANG C, JI H B, ZHANG J G. Improved CPHD filter for multitarget tracking[J]. Journal of Electronics & Information Technology, 2010, 32(9):2112-2118(in Chinese).
[19] 刘林. 航天器轨道理论[M]. 北京:国防工业出版社, 2000:99-326. LIU L. Orbit theory of spacecraft[M]. Beijing:National Defense Industry Press, 2000:99-326(in Chinese).
[20] VALLADO D, CRAWFORD P, HUJSAK R, et al. Revisiting spacetrack report #3[C]//AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Reston, VA:AIAA, 2006:1-88.
[21] VO B T, VO B N, CANTONI A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7):3553-3567.
[22] VO B N, MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4091-4104.
[23] JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3):401-422.
[24] SCHUHMACHER D, VO B T, VO B N. A Consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2008, 56(8):3447-3457.
/
| 〈 |
|
〉 |
CJA