Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking

ZHAO Xin; JI Hong-bing; YANG Bai-sheng

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Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking

更新时间：2025-07-16

- Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking
- Acta Electronica Sinica Vol. 39, Issue 3, Pages: 505-510(2011)
- 作者机构：
  
  1. 西安电子科技大学电子工程学院,陕西,西安,710071
  2. 中国航天科技集团公司第四研究院401所,陕西,西安,710071
  3. 西安电子科技大学电子工程学院陕西西安,710071
  4. 中国航天科技集团公司第四研究院401所陕西西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP302.7
- Published：2011
- 稿件说明：
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ZHAO Xin, JI Hong-bing, YANG Bai-sheng. Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking[J]. Acta Electronica Sinica, 2011, 39(3): 505-510.
DOI：

ZHAO Xin, JI Hong-bing, YANG Bai-sheng. Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking[J]. Acta Electronica Sinica, 2011, 39(3): 505-510. DOI：

摘要

针对大量杂波环境下数量变化的纯角度多目标航迹关联跟踪问题

提出一种新的基于Rao-Blackwellized粒子采样(RBPF)航迹关联的高斯混合概率假设密度(GMPHD)滤波算法.算法首先利用GMPHD在每时刻对多个目标组成的随机集合进行估计;然后利用基于随机有限集的RBPF对GMPHD所得到的目标集合进行检测和关联

有效解决GMPHD算法中无法进行多目标航迹识别的弊端;最后通过对所有粒子的融合完成航迹区分和估计.实验结果表明

提出方法比起目前经典的随机集Label-PHD关联跟踪算法

可以更有效的对数量未知的多目标航迹进行区分和关联估计

同时算法的跟踪性能及稳定性要好于Label-PHD算法.

Abstract

Due to the difficulty in association and estimation of multi-target tracks in the presence of data association uncertainty

clutter

noise and miss-detection.In this paper

a novel data association probability hypothesis density (PHD) filter for multi-target tracking based on Rao-Blackwellized particle filter (RBPF) algorithm is proposed.Firstly

the Gaussian mixture probability hypothesis density (GMPHD) filter has been proposed to estimate the set of all targets at every time step.Secondly

the data-association functionalities of RBPF can be incorporated with the PHD filter to produce the track-valued estimates of individual targets.Simulation results show that the proposed algorithm is more robust and accurate than Label-PHD algorithm which is very prevalent in the PHD tracking domains

also the proposed algorithm can estimate and distinguish each target more effective.

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