基于概率假设密度滤波方法的多目标跟踪技术综述

杨峰; 王永齐; 梁彦; 潘泉

doi:10.3724/SP.J.1004.2013.01944

基于概率假设密度滤波方法的多目标跟踪技术综述

doi: 10.3724/SP.J.1004.2013.01944

1.
西北工业大学自动化学院西安 710072

基金项目:

国家自然科学基金(61374159,61203224,61135001,61074179),中国航空科学基金(20125153027)资助

详细信息

作者简介:
杨峰西北工业大学副教授. 主要研究方向为信息融合, 目标跟踪, 雷达数据处理. E-mail: yangfeng@nwpu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-07-01
- 修回日期: 2013-08-28
- 刊出日期: 2013-11-20

A Survey of PHD Filter Based Multi-target Tracking

1.
School of Automation, Northwestern Polytechnical University, Xi'an 710072

Funds:

Supported by National Natural Science Foundation of China (61374159, 61203224, 61135001, 61074179), Aviation Science Foundation of China (20125153027)

摘要

摘要: 概率假设密度 (Probability hypothesis density, PHD) 滤波方法在多目标跟踪、交通管制、图像处理以及多传感器管理等领域得到了广泛关注. 本文对基于PHD滤波方法的多目标跟踪技术的产生、发展及研究现状进行了综述, 主要包括PHD滤波器、PHD执行方法、峰值提取及航迹提取技术、多传感器多目标跟踪及多传感器管理、 PHD平滑器以及多目标跟踪性能评价指标等, 并对PHD滤波器的相关应用进行介绍. 最后, 基于现有PHD滤波进展, 提出了PHD滤波技术在多目标跟踪领域需要重点关注的若干问题.
- 概率假设密度 /
- 多目标跟踪 /
- 贝叶斯滤波 /
- 峰值及航迹提取
Abstract: Probability hypothesis density (PHD) filter has attracted much attention in multi-target tracking, traffic control, image processing, multi-sensor management and other fields. An overview of the emergence, the development and the present research situation of the PHD filter in target tracking is presented here. Special attention is paid to the following areas: PHD filter, its implementation method, the peak and track extraction technology, multi-sensor multi-target tracking, multi-sensor management, PHD smoother, the assessment metrics of multi-target tracking performance, and also the relevant applications. Finally, based on the progress of existing PHD filters, some key issues which need to be focused on for PHD filters in multi-target tracking are introduced.
- Probability hypothesis density (PHD) /
- multi-target tracking /
- Bayesflter /
- peak and track extraction

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