Study of Multi-target Tracking and Data Association Based on Sequential Monte Carlo Algorithm

2008 ◽  
Author(s):  
Fan Lin-bo ◽  
Kang Li ◽  
Wu Ying-cheng ◽  
Zhao Ming
Keyword(s):  
Monte Carlo ◽  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Data Association ◽  
Monte Carlo Algorithm ◽  
Multi Target Tracking

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

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4226 ◽  
Author(s):  
Rang Liu ◽  
Hongqi Fan ◽  
Tiancheng Li ◽  
Huaitie Xiao
Keyword(s):  
Monte Carlo ◽  
Computational Complexity ◽  
Monte Carlo Method ◽  
State Estimation ◽  
Target Tracking ◽  
Computational Efficiency ◽  
Sequential Monte Carlo ◽  
Computationally Efficient ◽  
Sequential Monte Carlo Method ◽  
Multi Target Tracking

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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