基于聚类和Hough变换的多编队航迹起始算法
DOI:
作者:
作者单位:

(海军航空工程学院 电子信息工程系,山东 烟台 264001)

作者简介:

通讯作者:

中图分类号:

TN401;TP391

基金项目:


Dense Multi-Formation Track Initiation Algorithm Based on K-Means Clustering and Hough Transform
Author:
Affiliation:

(Department of Electronic and Information Engineering,NAAU,Yantai Shandong 264001,China)

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    以传感器分辨力较低、编队成员之间距离较近时,不能辨别出编队中的目标数量和队形结构为背景,集中研究了密集杂波下编队的航迹起始问题并分析了该问题的难点所在;在此基础上,首先,通过循环阈值法将前3个时刻的量测点迹分割成多个子群;然后,对分割后的各个子群运用K均值聚类法,更准确地求出各个子群的聚类中心点作为该子群的中心;最后,通过最近邻法,以最大速度为限制将前3个时刻各子群中心点互联,对互联上的中心点经Hough变换,映射到参数空间,从而判别出成功起始的航迹和速度。经仿真验证,该方法对密集杂波环境下编队的航迹起始效果较好,并且速度快,起始率高。

    Abstract:

    In this paper, the problem of formation track initiation was studied against a background, which had a dense clutter. Especially when the sensor had a low resolution, which couldn’t distinguish the number and structure of the formation, then the difficult points were analyzed. In order to initiate the formation tracks, the measurements obtained from the first three frames were separated as several sub-groups using the threshold repetition method at first. Then the k-means clustering method was applied to the sub-groups, the center of the clustering qua the center of sub-groups. At last, nearest neighbor method was used to associate the center points of sub-groups, the points that were transformed to the parameter space, so the tracks and velocities of the successfully initiated tracks were estimated. The simulation result proved the method had a better effect on dense clutter formation track initiation, and had both short track initiation time and high track initiation probability.

    参考文献
    相似文献
    引证文献
引用本文

邢凤勇,熊伟,王海鹏.基于聚类和Hough变换的多编队航迹起始算法[J].海军航空大学学报,2010,25(6):624-628
XING Feng-yong, XIONG Wei, WANG Hai-peng. Dense Multi-Formation Track Initiation Algorithm Based on K-Means Clustering and Hough Transform[J]. JOURNAL OF NAVAL AVIATION UNIVERSITY,2010,25(6):624-628

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2018-07-05
  • 出版日期: