Measurement Driven Birth Model for the Generalized Labeled Multi-Bernoulli Filter

被引：0
|
作者：
Lin, Shoufeng
Vo, Ba Tuong
Nordholm, Sven E.
机构：
来源：
2016 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS) | 2016年
关键词：
measurement driven birth; generalized labeled multi-Bernoulli filter; tracking filter; Bayes recursion; random finite set; multi-target tracking; RANDOM FINITE SETS; IMPLEMENTATIONS; PHD;
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 presents a measurement driven birth (MDB) model for the generalized labeled multi-Bernoulli (GLMB) filter. The MDB model adaptively generates target births based on measurement data, thereby eliminating the dependence of a priori knowledge of target birth distributions. Numerical results are provided to demonstrate the performance.
引用
收藏
页码：94 / 99
页数：6
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