箱粒子广义标签多伯努利滤波的目标跟踪算法

苗雨; 宋骊平; 姬红兵

doi:10.7652/xjtuxb201710018

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箱粒子广义标签多伯努利滤波的目标跟踪算法

更新时间：2025-07-09

- 箱粒子广义标签多伯努利滤波的目标跟踪算法
- Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering
- 西安交通大学学报 2017年51卷第10期页码：107-112
- 作者机构：
  
  西安电子科技大学电子工程学院,西安,710071
- 作者简介：
- 基金信息：
  
  : 2017-02-15。作者简介: 苗雨(1992—),女,硕士生;姬红兵(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61372003)
- DOI：10.7652/xjtuxb201710018
  中图分类号： TN953
- 网络首发：2017-10-10，
  
  纸质出版：2017
- 稿件说明：
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苗雨, 宋骊平, 姬红兵. 箱粒子广义标签多伯努利滤波的目标跟踪算法[J]. 西安交通大学学报, 2017,51(10):107-112.

Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering[J]. 2017, 51(10): 107-112.
苗雨, 宋骊平, 姬红兵. 箱粒子广义标签多伯努利滤波的目标跟踪算法[J]. 西安交通大学学报, 2017,51(10):107-112. DOI： 10.7652/xjtuxb201710018.

Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering[J]. 2017, 51(10): 107-112. DOI： 10.7652/xjtuxb201710018.

摘要

针对序列蒙特卡罗广义标签多伯努利滤波(SMC-GLMB)算法计算效率低、实时性差的问题

提出了箱粒子广义标签多伯努利滤波的目标跟踪(Box-GLMB)算法。该算法使用带标签的随机有限集描述多目标的状态

包括目标的位置和速度

并且对每个目标用互不相同的标签进行区分; 然后利用箱粒子滤波算法近似单目标状态的概率密度

即用一组带权值的均匀分布拟合单目标状态概率密度; 最后通过广义标签多伯努利滤波对多目标状态的概率密度进行预测与更新

从多目标状态后验概率密度中估计单目标的位置与速度

根据目标的标签可以实现航迹跟踪。Box-GLMB算法结合了箱粒子滤波与GLMB算法的优势

能够跟踪目标航迹

同时提高计算效率。仿真结果表明

Box-GLMB算法可以有效估计目标状态以及跟踪目标航迹

相比于SMC-GLMB算法

计算效率提升了62%。

Abstract

A target tracking method with box-particle generalized label multi-Bernoulli filtering(Box-GLMB)is proposed to address the problem that the sequential Monte Carlo generalized label multi-Bernoulli filter(SMC-GLMB)has low computation efficiency and weak real-time performance. A random finite set with labels is employed to describe states of multi-targets

including positions and velocities of targets

and targets are distinguished by different labels. Then the box-particle filter algorithm is used to approximate the probability density of single target

that is

the probability density of single target is approximated by weighted uniform distributions. Multi-target densities are predicted and updated by using the generalized label multi-Bernoulli filter(GLMB). Target states are estimated from posterior probability density and trajectories of targets are tracked by labels. The proposed method combines the advantages of the box-particle filter and the generalized label multi-Bernoulli filter. Box-GLMB filter is able to track trajectories and to improve computational efficiency at the same time. Simulation results show that the proposed filter effectively estimate states of targets and track trajectories. A comparison with the SMC-GLMB filter show that the proposed filter increases 62% computational efficiency.

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references

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