Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering

doi:10.7652/xjtuxb201710018

您当前的位置：

首页 >

文章列表页 >

Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering

更新时间：2025-07-09

- Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering
- Vol. 51, Issue 10, Pages: 107-112(2017)
- 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：
- DOI：10.7652/xjtuxb201710018
  CLC： TN953
- Online First：10 October 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
Target Tracking Method with Box-Particle Generalized Label Multi-Bernoulli Filtering[J]. 2017, 51(10): 107-112.
DOI：

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.

关键词

Keywords

references

MAHLER R. Statistical multisource multitarget information fusion [M]. Norwood, MA, USA: Artech House, 2007: 539-682.

张光华, 连峰, 韩崇昭, 等. 高斯混合扩展目标多伯努利滤波器 [J]. 西安交通大学学报, 2014, 48(10): 9-14.

ZHANG Guanghua, LIAN Feng, HAN Chongzhao, et al. A Gaussian mixture extended-target multi-Bernoulli filter [J]. Journal of Xi'an Jiaotong University, 2014, 48(10): 9-14.

VO B T, VO B N. Labeled random finite sets and multi-object conjugate priors [J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3460-3475.

VO B N, VO B T, PHUNG D. Labeled random finite sets and the Bayes multi-target tracking filter [J]. IEEE Transactions on Signal Processing, 2014, 62(24): 6554-6567.

ABDALAH F, GNING A, BONNIFAIT P. Box particle filtering for nonlinear state estimation using interval analysis [J]. Automatica, 2008, 44(3): 807-815.

JAULIN L. Applied interval analysis [M]. Berlin, Germany: Springer, 2001: 11-43.

ANDREW M. Applied interval analysis: with examples in parameter and state estimation, robust control and robotics [J]. Kybernetes, 2002, 31(5): 117-23.

GNING A, RISTIC B, MIHAYLOVA L. Bernoulli particle/box-particle filters for detection and tracking in the presence of triple measurement uncertainty [J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2138-2151.

SCHIKOR M, GNING A, MIHAYLOVA L, et al. Box-particle probability hypothesis density filtering [J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(3): 1660-1672.

SONG L, LIANG M, JI H. Box-particle implementation and comparison of cardinalized probability hypothesis density filter [J]. Radioengineering, 2016, 25(1): 177-186

邱昊, 黄高明, 左炜, 等. 基于标签多伯努利滤波的衍生目标跟踪算法 [J]. 航空学报, 2015, 36(9): 3012-3019.

QIU Hao, HUANG Gaoming, ZUO Wei, et al. Spawned target tracking algorithm based on labeled multi-Bernoulli filtering [J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(9): 3012-3019.

赵雪刚, 宋骊平, 姬红兵. 量化量测条件下的交互多模型箱粒子滤波 [J]. 西安电子科技大学学报, 2014, 41(6): 37-44.

ZHAO Xuegang, SONG Liping, JI Hongbing. Interacting multiple model box particle filter with quantitative measurements [J]. Journal of Xidian University, 2014, 41(6): 37-44.

RISTIC B, VO B N, CLARK D, VO B T. A metric for performance evaluation of multi-target tracking algorithms [J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3452-3457.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Tracking and Locating Algorithm of 3-Dimensional Moving Targets Using 2-Dimensional Angular Measurements from Multiple Passive Sensors

A Fixed-Interval Smoothing Algorithm for Linear Systems with General Correlated Noises

New Parameter Estimation Method for Flying Target Based on Bearing Measurements

A New Parameter Estimation Method for Flying Target Using Bearing Measurement

Data Association Method Based on Regression Analysis

Related Author

孙国基 1

姚东升 3

赵军 3

卫军胡 1

刘哲 1

Related Institution

Mailbox 21 Ext. 187

State Key Laboratory for Manufacturing System Engineering, Xi'an Jiao tong University

Systems Engineering Institute, Xi'an Jiaotong University

China Satellite Maritime Tracking and Control Department, Jiangyin

School of Electronics and Information Engineering, Xi'an Jiaotong University

⁰