基于统计双门限的中断航迹配对关联算法

齐林; 王海鹏; 刘瑜

doi:10.12000/JR14077

基于统计双门限的中断航迹配对关联算法

DOI: 10.12000/JR14077

(海军航空工程学院信息融合研究所烟台 264001)

基金项目:

山东省自然科学基金青年基金(ZR2012FQ004)资助课题

详细信息

作者简介:
齐林(1989-),男,海军航空工程学院博士生,主要研究方向为多传感器信息融合.E-mail:3278pirate@163.com王海鹏(1985-),男,博士,讲师,海军航空工程学院信息融合研究所,主要研究方向为群目标跟踪、多传感器信息融合.E-mail:armystudent@sohu.com刘瑜(1986-),男,博士,讲师,海军航空工程学院信息融合研究所,主要研究方向为状态一致估计、多传感器信息融合.E-mail:liuyu77360132@126.com

通讯作者:
齐林3278pirate@163.com

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出版历程
- 收稿日期: 2014-05-07
- 修回日期: 2014-06-13
- 网络出版日期: 2015-06-28

Track segment association algorithm based on statistical binary thresholds

(Institute of Information Fusion, Naval Aeronautical and Astronautically University, Yantai 264001, China)

摘要

摘要: 针对现有经典的中断航迹关联算法在目标密集、航迹交叉或分岔环境下关联正确率低和实用性差的问题,该文提出了基于统计双门限的中断航迹配对关联算法.该算法引入统计双门限原理,增加了2 分布门限检测的关联样本数,对复杂环境具有更强的适应能力.仿真验证表明,在空中飞行目标航迹交叉环境和弹道目标环境下,该文算法的全局正确关联率和平均正确关联率均比经典算法有显著提高,验证了该算法性能的优越性.
- 航迹关联 /
- 中断航迹 /
- 统计双门限 /
- 正确关联率
Abstract: The classical Track Segment Association (TSA) algorithm suffers from low accuracy and is impractical to use in concentrated targets, branching, and cross-tracking environment. Thus, a new statistical binary track segment association algorithm is proposed. The new algorithm is more appropriate as it increases the sample size for the 2 distribution threshold detection. Simulation results show that in air cross tracking and for ballistic targets, the global correct association rate and the average correct association rate of the proposed algorithm are remarkably improved, which proves the good performance of the proposed algorithm.
- Track-to-track association /
- Track segment /
- Statistical binary thresholds /
- Correct association rate

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参考文献(17)

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