Particle PHD filter algorithm for multisensor  multitarget tracking

HAO Yan-ling1; MENG Fan-bin1; ZHANG Chong-meng2; CAI Yi-feng2; WANG Su-xin2

doi:10.13873/j.1000-97872010.04.015

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Particle PHD filter algorithm for multisensor multitarget tracking

更新时间：2025-09-11

- Particle PHD filter algorithm for multisensor multitarget tracking
- Vol. 29, Issue 4, Pages: 18-21+25(2010)
- 作者机构：
  
  1. 哈尔滨工程大学自动化学院
  2. 天津航海仪器研究所
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872010.04.015
  CLC： TN713
- Published：2010
- 稿件说明：
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[1]郝燕玲,孟凡彬,张崇猛,蔡艺峰,王素鑫.多传感器多目标跟踪的粒子PHD滤波算法[J].传感器与微系统,2010,29(04):18-21+25.

HAO Yan-ling1, MENG Fan-bin1, ZHANG Chong-meng2, et al. Particle PHD filter algorithm for multisensor multitarget tracking[J]. 2010, 29(4): 18-21+25.
[1]郝燕玲,孟凡彬,张崇猛,蔡艺峰,王素鑫.多传感器多目标跟踪的粒子PHD滤波算法[J].传感器与微系统,2010,29(04):18-21+25. DOI： 10.13873/j.1000-97872010.04.015.

HAO Yan-ling1, MENG Fan-bin1, ZHANG Chong-meng2, et al. Particle PHD filter algorithm for multisensor multitarget tracking[J]. 2010, 29(4): 18-21+25. DOI： 10.13873/j.1000-97872010.04.015.

摘要

针对单传感器跟踪系统的缺陷

提出了基于粒子概率假设密度（PHD）滤波的多传感器多目标跟踪算法。这种算法不仅避免了多传感器多目标跟踪的数据关联问题

而且在漏检、目标密集、航迹交叉、小范围内目标数多的杂波环境下能够稳定、精确地估计目标状态和目标数。仿真实验比较了单传感器粒子PHD滤波与多传感器的粒子PHD滤波的跟踪性能

验证了该方法的跟踪性能和精度。

Abstract

In order to avoid the flaw of single-sensor tracking system

a multisensor multitarget tracking algorithm based on particle probability hypothesis density（PHD） filter is proposed.This algorithm can not only avoid data association problem of multisensor multitarget tracking

but also can track a time-varying number of targets robustly and their states under miss detection

dense target

cross track and large number of targets under clutter environment

achieving real-time performance.Simulation result validated multisensor with particle PHD filter performance and compared single sensor particle PHD with and multisensor with particle PHD filter performance.

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references

随机集理论及其在信息融合中的应用 [J]. 彭冬亮,文成林,徐晓滨,薛安克. 电子与信息学报 . 2006(11)

时间序列分析的随机集方法 [J]. 陈良洲,施文康. 上海交通大学学报 . 2005(03)

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