Challenges of Multi-Target Tracking for Space Situational Awareness

被引:0
|
作者
Jones, Branson A. [1 ]
Bryant, Daniel S. [1 ]
Vo, Ba-Tuong [2 ]
Vo, Ba-Ngu [2 ]
机构
[1] Univ Colorado, Dept Aerosp Engn Sci, Boulder, CO 80309 USA
[2] Curtin Univ, Dept Elect & Comp Engn, Perth, WA, Australia
来源
2015 18TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2015年
关键词
RANDOM FINITE SETS; PROPAGATION; DEBRIS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The tracking of space objects poses unique challenges when compared to traditional applications. Direct application of standard multi-target tracking models fails to yield accurate results for the case of space objects. For example, dynamic models for traditional applications require simple, often linear, discrete-time models. This is not the case for space objects where motion is nonlinear and computation cost is too prohibitive for current Monte Carlo filters. This paper describes the multi target tracking problem for space objects and summarizes system requirements for space situational awareness. Traditional multi target filter models are compared to corresponding methods for space object tracking in the context of tracking performance via the 6-Generalized Labeled Multi -Bernoulli (6-GLMB) filter. Using these tests, key research challenges for space situational awareness are discussed.
引用
收藏
页码:1278 / 1285
页数:8
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