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多无源传感器去相关数据关联算法

鹿传国 冯新喜 孔云波 张迪

鹿传国, 冯新喜, 孔云波, 张迪. 多无源传感器去相关数据关联算法. 自动化学报, 2014, 40(3): 497-505. doi: 10.3724/SP.J.1004.2014.00497
引用本文: 鹿传国, 冯新喜, 孔云波, 张迪. 多无源传感器去相关数据关联算法. 自动化学报, 2014, 40(3): 497-505. doi: 10.3724/SP.J.1004.2014.00497
LU Chuan-Guo, FENG Xin-Xi, KONG Yun-Bo, ZHANG Di. Decorrelation-based Data Association Algorithm for Multi-passive-sensor System. ACTA AUTOMATICA SINICA, 2014, 40(3): 497-505. doi: 10.3724/SP.J.1004.2014.00497
Citation: LU Chuan-Guo, FENG Xin-Xi, KONG Yun-Bo, ZHANG Di. Decorrelation-based Data Association Algorithm for Multi-passive-sensor System. ACTA AUTOMATICA SINICA, 2014, 40(3): 497-505. doi: 10.3724/SP.J.1004.2014.00497

多无源传感器去相关数据关联算法

doi: 10.3724/SP.J.1004.2014.00497
基金项目: 

陕西省自然科学基金(2011JM8023)资助

详细信息
    作者简介:

    冯新喜 空军工程大学信息与导航学院教授. 主要研究方向为信息融合, 指挥自动化信息处理.E-mail:fengxinxi2005@yahoo.com.cn

    通讯作者:

    鹿传国

Decorrelation-based Data Association Algorithm for Multi-passive-sensor System

Funds: 

Support by Natural Science Foundation of Shannxi (2011JM8023)

  • 摘要: 对基于多维分配模型的多无源传感器(Multi-passive-sensor system,MPSS)多目标数据关联算法进行了归纳分析,指出该模型不仅忽略了极大似然估计所引入的随机误差,而且未充分考虑量测与伪量测之间的相关性.继而建立了一种去相关修正数据关联模型,并提出利用无迹变换计算二者之间的互协方差. 另外定义了概念解的区分度来评估关联代价构造的合理性. 最后进行了仿真实验,结果表明去相关后的关联代价能更精准地反映数据关联的可能性,所提关联算法运算时间有所增加,但关联性能更佳.
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出版历程
  • 收稿日期:  2012-11-06
  • 修回日期:  2013-05-14
  • 刊出日期:  2014-03-20

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