Centralized Multi-Sensor Multi-Target Tracking with Labeled Random Finite Sets

被引：0
|
作者：
Wei, Baishen [1 ]
Nener, Brett [1 ]
Liu, Weifeng [2 ]
Ma, Liang [3 ]
机构：
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA, Australia
[2] Hangzhou Dianzi Univ, Sch Automat, Hangzhou, Zhejiang, Peoples R China
[3] Harbin Engn Univ, Coll Mech & Elect Engn, Harbin, Peoples R China
来源：
2016 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS) | 2016年
关键词：
delta-GLMB; centralized; sensor network; association map; VISUAL TRACKING; FILTER; ALGORITHM; ORDER;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper addresses the problem of multi-sensor multi-target tracking. The main contribution is an efficient implementation of the multi-sensor delta-Generalized labeled Multi-Bernoulli (delta-GLMB) update. To truncate the weighted sums of the multi-target exponentials, the ranked assignment algorithm is used in the update to determine the most important terms without computing all the terms. Simulation experiments via linear Gaussian mixture models confirm the effectiveness of the proposed algorithm.
引用
收藏
页码：82 / 87
页数：6
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