Multi-sensor tracking with partly overlapping FoV using detection field of probability modeling and the GLMB filter

被引：3
|
作者：
Liu, Weifeng [1 ,2 ]
Liu, Qiliang [1 ]
Chen, Yimei [2 ]
Cui, Hailong [2 ]
机构：
[1] Shaanxi Univ Sci & Technol, Sch Elect & Control Engn, Xian, Peoples R China
[2] Hangzhou Dian Univ, Sch Automat, Hangzhou, Peoples R China
来源：
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING | 2023年 / 2023卷 / 01期
关键词：
Target tracking; Multi-sensor; Partly overlapping FoV; Labeled random finite sets; The GLMB filter; RANDOM FINITE SETS; TARGET TRACKING; PHD FILTERS; MULTITARGET; FUSION; ALLOCATION; ALGORITHM;
D O I：
10.1186/s13634-022-00962-4
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：
In this paper, we consider multi-sensor with partly overlapping field of view (FoV) in the labeled random finite set (L-RFS) framework. This is different from most existing multi-sensor tracking algorithms, where the sensors are assumed to have the same FoV. We describe the partly overlapping FoV by modeling probability field of detection for individual sensors in whole observation area and can be seen as the same range of FoV. We consider all these using generalized labeled multi-Bernoulli filter in labeled RFS framework. Besides, we also propose a measurement-driven target birth model. Finally, the effectiveness of the proposed algorithm is verified by experiments.
引用
收藏
页数：21
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