Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

被引：4
|
作者：
Du, Haocui [1 ]
Xie, Weixin [1 ]
机构：
[1] Shenzhen Univ, Automat Target Recognit ATR Key Lab, Shenzhen 518060, Peoples R China
来源：
SENSORS | 2020年 / 20卷 / 18期
基金：
中国国家自然科学基金;
关键词：
extended target tracking; gamma-Gaussian-inverse Wishart; Poisson multi-Bernoulli mixture; MULTITARGET TRACKING; BAYESIAN-APPROACH; OBJECT TRACKING;
D O I：
10.3390/s20185387
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter.
引用
收藏
页码：1 / 15
页数：15
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