A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking

被引：7
|
作者：
Liu, Rang [1 ]
Fan, Hongqi [1 ]
Li, Tiancheng [2 ]
Xiao, Huaitie [1 ]
机构：
[1] Natl Univ Def Technol, Coll Elect Sci, Natl Key Lab Sci & Technol ATR, Changsha 410073, Hunan, Peoples R China
[2] Northwestern Polytech Univ, Sch Automat, Minist Educ, Key Lab Informat Fus Technol, Xian 710072, Shaanxi, Peoples R China
来源：
SENSORS | 2019年 / 19卷 / 19期
基金：
中国国家自然科学基金;
关键词：
random finite set; bayes smoother; labeled multi-Bernoulli; multi-target tracking; Sequential Monte Carlo; RANDOM FINITE SETS; MODEL;
D O I：
10.3390/s19194226
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
A forward-backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.
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收藏
页数：21
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