[1]唐孟麒,李波,郝丽君.面向扩展目标跟踪的网格聚类量测划分方法[J].智能系统学报,2022,17(4):806-813.[doi:10.11992/tis.202109013]
TANG Mengqi,LI Bo,HAO Lijun.Grid clustering measurement set partition method for extended target tracking[J].CAAI Transactions on Intelligent Systems,2022,17(4):806-813.[doi:10.11992/tis.202109013]
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TANG Mengqi,LI Bo,HAO Lijun.Grid clustering measurement set partition method for extended target tracking[J].CAAI Transactions on Intelligent Systems,2022,17(4):806-813.[doi:10.11992/tis.202109013]
面向扩展目标跟踪的网格聚类量测划分方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
806-813
栏目:
学术论文—机器感知与模式识别
出版日期:
2022-07-05
- Title:
- Grid clustering measurement set partition method for extended target tracking
- Keywords:
- extended target; measurement set; grid clustering; time-space correlation; fuzzy C-mean; survival target; newborn target; probability hypothesis density
- 分类号:
- TN713;TP39
- 摘要:
- 针对扩展目标跟踪中量测集划分困难及目标数目估计不准的问题，提出了一种面向扩展目标跟踪的网格聚类量测集划分方法。首先，由目标之间的时空关联性，将当前时刻的量测划分为存活目标量测与新生目标量测。然后，针对高斯混合概率假设密度滤波器与扩展目标高斯混合概率假设密度滤波器，分别推导出改进的模糊C均值算法与改进的网格聚类算法用于划分存活目标量测集与新生目标量测集。仿真结果表明本文方法可实现量测集的准确划分，有效完成扩展目标跟踪，避免了漏检与过检。
- Abstract:
- To address the issues of difficult measurement set partitioning and inaccurate estimation of the number of targets in extended target tracking, we suggest a grid clustering measurement set partitioning approach for extended target tracking. Firstly, the current moment measurement is classified into two categories based on the time-space correlation between the targets: survival-target measurement and newborn-target measurement. Then, an improved fuzzy C-means algorithm and an improved grid clustering algorithm are derived for the Gaussian mixture probability hypothesis density filter and the extended target Gaussian mixture probability hypothesis density filter, respectively, which are employed to separate the viable target set and the new target set. The simulation results show that the proposed techniques can accurately divide the measurement set, effectively complete the extended target tracking, and avoid the missed and over-checked measurements.
- 参考文献/References:
-
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备注/Memo
收稿日期:2021-09-03。
基金项目:辽宁省自然科学基金面上项目（2020-MS-292）; 国家自然科学 基金面上项目（51679116）.
作者简介:唐孟麒，硕士研究生，主要研究方向为信号与信息处理;李波，教授，博士，主要研究方向为现代信号处理与信息融合。主持完成国家自然科学基金等纵向项目7项。发表学术论文90余篇，授权各类专利10余项;郝丽君，硕士研究生，主要研究方向为信号与信息处理
通讯作者:李波. E-mail：leeboo@yeah.net
更新日期/Last Update:
1900-01-01