Maneuvering Multi-target Tracking Using the Multi-model Cardinalized Probability Hypothesis Density Filter

被引：0

作者：

Fu Yaowen ^{[1
]}

Long Jianqian ^{[1
]}

Yang Wei ^{[1
]}

机构：

[1] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China

来源：

CHINESE JOURNAL OF ELECTRONICS | 2013年 / 22卷 / 03期

关键词：

Multi-target tracking; Maneuvering target tracking; Multi-model method; Cardinalized probability hypothesis density (CPHD) filter;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Tracking an unknown and time-varying number of maneuvering targets is a challenging problem in the presence of noise, clutter, uncertainties in target maneuvers, data association, and detection. To account for this problem, a multi-model extension of the Cardinalized probability hypothesis density (CPHD) filter is proposed in this paper. Additionally, a particle implementation and a Gaussian mixture implementation of the proposed extension are given for generic models and linear Gaussian models, respectively. The effectiveness of the extension is illustrated through Monte Carlo simulation.

引用

页码：634 / 640

页数：7

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