基于广义标签多伯努利滤波的可分辨群目标跟踪算法

朱书军; 刘伟峰; 崔海龙

doi:10.16383/j.aas.2017.c160334

基于广义标签多伯努利滤波的可分辨群目标跟踪算法

doi: 10.16383/j.aas.2017.c160334

朱书军^1,,
刘伟峰^1,2, ,,
崔海龙^1,

1.
杭州电子科技大学自动化学院系统科学与控制工程研究所杭州 310018
2.
信息系统工程重点实验室南京 210007

基金项目:

国家自然科学基金 61271144

国家自然科学基金 61402140

浙江省自然科学基金 LY15F030020

国家自然科学基金 61273170

国家自然科学基金 61333011

详细信息

作者简介:
朱书军杭州电子科技大学自动化学院系统科学与控制工程研究所硕士研究生.2014年获得丽水学院学士学位.主要研究方向为目标跟踪与信息融合.E-mail:zsjun92@163.com

崔海龙杭州电子科技大学自动化学院系统科学与控制工程研究所硕士研究生.2014年获得安徽工程大学学士学位.主要研究方向为目标跟踪与信息融合.E-mail:cuihailong_86@163.com

通讯作者:
刘伟峰杭州电子科技大学副教授.主要研究方向为目标跟踪, 不确定信息处理与模式识别.本文通信作者.E-mail:liuwf@hdu.edu.cn

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- 被引次数: 28
出版历程
- 收稿日期: 2016-04-15
- 录用日期: 2016-12-27
- 刊出日期: 2017-12-20

Multiple Resolvable Groups Tracking Using the GLMB Filter

ZHU Shu-Jun^1
,,
LIU Wei-Feng^{1,2
, ,},
CUI Hai-Long^1
,

1.
Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018
2.
Science and Technology on Information System Engineering Laboratory, Nanjing 210007

Funds:

National Natural Science Foundation of China 61271144

National Natural Science Foundation of China 61402140

Natural Science Foundation of Zhejiang Province LY15F030020

National Natural Science Foundation of China 61273170

National Natural Science Foundation of China 61333011

More Information

Author Bio:
Master student at the Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University. He received his bachelor degree from Lishui University in 2014. His research interest covers target tracking and information fusion

Master student at the Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University. He received his bachelor degree from Anhui Polytechnic University in 2014. His research interest covers target tracking and information fusion

Corresponding author: LIU Wei-Feng Associate professor at Hangzhou Dianzi University. His research interest covers target tracking, uncertain information processing, and pattern recognition. Corresponding author of this paper

摘要

摘要: 针对杂波条件下可分辨群目标的状态估计、目标个数与子群个数估计问题，提出了一种基于标签随机有限集（Label random finite set，L-RFS）框架下的可分辨群目标跟踪算法，该算法主要包括两个方面：可分辨多群目标动态建模和多群目标的跟踪估计.本文工作主要包括：1）结合图论中的邻接矩阵对可分辨群目标运动进行动态建模.2）利用基于L-RFS的广义标签多伯努利滤波（Generalizes label multi-Bernoulli，GLMB）算法对目标的状态和个数进行估计，并且通过估计邻接矩阵得到群的结构和个数估计.3）通过个数不同、结构不同的三个子群目标在二维平面分别做线性和非线性运动进行算法验证.仿真分析表明本文算法能够准确估计出群目标中各目标的状态、个数以及子群的个数，并且能获得目标的航迹估计.
- 可分辨群目标跟踪 /
- 广义标签多伯努利滤波 /
- 邻接矩阵 /
- 随机有限集 /
- 图论
Abstract: Aiming at the estimation of states, the number of targets and subgroups, a resolvable group target tracking algorithm is proposed based on the framework of label random finite set (L-RFS). The proposed algorithm focus on two aspects:dynamic modeling and tracking estimation for multiple resolvable group targets. Specifically, in the first step, the adjacent matrix is fused in the dynamic models. In the second step, the estimated state sets of the targets and the number of targets are estimated by using the generalized labeled multi-Bernoulli (GLMB) filter in the L-RFS framework. Finally, from the estimated adjacent matrix, the structures and number of subgroups are shown. Two experiments of a linear system and a nonlinear system, which involve three groups of targets with different shapes and structure, are given to show that the given algorithm is effective in estimating the resolvable group targets.
- Resolvable group target tracking /
- generalized label multi-Bernoulli (GLMB) /
- adjacent matrix /
- random finite set (RFS) /
- graph theory
注释:

1) 本文责任编委郭戈

HTML全文

本文责任编委郭戈

图 1 群目标(“+”表示量测)

Fig. 1 The group target ("+" denotes measurement)

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图 2 扩展目标(“+”表示量测)

Fig. 2 The extended target ("+" denotes measurement)

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图 3 群目标结构模型

Fig. 3 The structure model of group target

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图 4 目标之间依赖关系

Fig. 4 The dependencies of targets

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图 5 三个群目标结构

Fig. 5 Three group targets structure

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图 6 多群目标真实轨迹

Fig. 6 The true tracks of groups

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图 7 由CBMeMBer滤波器得到的轨迹估计

Fig. 7 Track estimation by CBMeMBer filter

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图 8 由GLMB滤波器得到的轨迹估计

Fig. 8 Track estimation by GLMB filter

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图 9 由GLMB滤波算法得到状态估计

Fig. 9 The state estimation by GLMB filter

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图 10 OSPA距离对比图（经50次MC平均）

Fig. 10 50 MC run average of the compare of OSPA

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图 11 目标个数估计

Fig. 11 The estimated number of targets

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图 12 子群个数估计

Fig. 12 The estimated number of groups

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图 13 多群目标真实轨迹

Fig. 13 The true tracks of groups

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图 14 由GLMB滤波算法得到的状态估计

Fig. 14 The state estimation by GLMB filter

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图 15 目标个数估计

Fig. 15 The estimated number of targets

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图 16 OSPA距离对比（经50次MC平均）

Fig. 16 The OSPA distance (50 MCs)

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图 17 群的个数估计

Fig. 17 The estimated number of groups

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表 1 算法性能分析

Table 1 Performance analysis of algorithms

算法	GLMB算法		CBMeMBer算法
算法	线性	非线性	线性	非线性
时间(秒/步)	1.35	2	0.044	0.52

下载: 导出CSV

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