计算机科学

计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 321-326.doi: 10.11896/jsjkx.2004000145

• 计算机网络 • 上一篇    下一篇

动态自适应的多雷达信息加权融合方法

张良成1,2, 王运锋1   

  1. 1 四川大学计算机学院 成都 610065
    2 成都运为科技有限公司 成都 610042
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 王运锋(yfwang@scu.edu.cn)
  • 作者简介:zhangliangcheng@stu.scu.edu.cn
  • 基金资助:
    四川省科技厅厅项目(2019JDRC0042)

Dynamic Adaptive Multi-radar Tracks Weighted Fusion Method

ZHANG Liang-cheng1,2, WANG Yun-feng1   

  1. 1 College of Computing,Sichuan University,Chengdu 610065,China
    2 Chengdu Yunwei Technology Co.,Ltd.,Chengdu 610042,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHANG Liang-cheng,born in 1996,postgraduate.His main research interests include multi-source information fusion and target tracking.
    WANG Yun-feng,born in 1975,Ph.D.His main research interests include multisource information fusion and big data analysis.
  • Supported by:
    This work was supported by the Project of Department of Science and Technology of Sichuan Province(2019JDRC0042).

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摘要： 为利用多源探测雷达航迹数据融合形成精度更高的航迹数据,对多源信息融合理论方法进行研究,结合雷达目标跟踪技术应用需求,基于经典动态加权方法与卡尔曼滤波技术,提出并设计一种动态自适应的多源雷达信息加权融合方法。在不事先预知雷达探测精度及探测环境的条件下,为弥补静态权值分配加权融合方法的缺点,通过设立4种反应数据源质量特点的子项权值,实时分析雷达航迹报告的质量因子,并依据质量因子情况动态地完成多源数据融合,从而得到综合精度优于雷达数据源的融合航迹结果。实测及仿真结果表明,所提方法具有提升精度的特点,并具有可用性,稳健性较好。

关键词: 动态加权融合, 多源信息融合, 卡尔曼滤波, 目标跟踪

Abstract: In order to form a more accurate fused track by using multi-source radar track data,the theoretical method of multi-source information fusion classical dynamic weighting method and Kalman filter technology are studied.A dynamic adaptive weighted fusion method of multi-source radar information is designed.To overcome the disadvantage of the static assignment weighted fusion method when the radar detection accuracy and detection environment are unknown,setting up a quality factor which contains 4 subitem weights that reflect the quality characteristics of the data source,and real-time analysis of the quality of radar track reports.Depending on the quality factor to complete multi-source fusion dynamically,and obtain better accuracy fusion track.After practical testing and simulation test,it proves that this method is effective and steady.

Key words: Kalman filtering, Multi-source information fusion, Target tracking, Weighting fusion

中图分类号: 

  • TP301
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