CAJ | 학술논문

为提高目标航迹相交和近距平行状态时航迹关联的正确率，提出了一种基于K-中心点聚类的模糊航迹关联算法。该算法基于K-中心点聚类算法，将系统航迹作为聚类中心，采用局部航迹与系统航迹关联的策略，为描述航迹间的相似性，采用模糊分析方法，综合考虑各个因素的影响，构造模糊关联矩阵，并利用历史信息和先验知识进行航迹关联。仿真表明该算法在航迹相交状态下，相交时刻关联正确率比K-medoids聚类算法提高5%左右，近距平行状态下关联正确率的收敛速度优于K-medoids聚类算法。
위제고목표항적상교화근거평행상태시항적관련적정학솔，제출료일충기우K-중심점취류적모호항적관련산법。해산법기우K-중심점취류산법，장계통항적작위취류중심，채용국부항적여계통항적관련적책략，위묘술항적간적상사성，채용모호분석방법，종합고필각개인소적영향，구조모호관련구진，병이용역사신식화선험지식진행항적관련。방진표명해산법재항적상교상태하，상교시각관련정학솔비K-medoids취류산법제고5%좌우，근거평행상태하관련정학솔적수렴속도우우K-medoids취류산법。
To improve the probability of correct track association when tracks of targets intersect or are in parallel within near range, a fuzzy track association algorithm based on K-center clustering was presented. With system track as clustering center, the algorithm associated sensor tracks with system tracks, and thus improved the efficiency of the algorithm considerably. Fuzzy set theory is applied in the algorithm to describe the similarity of tracks and evaluate the influence of various factors. Accordingly a fuzzy association matrix was built and history information and priori knowledge were used to associate tracks. The simulation results demonstrate five percent higher accuracy at the intersection point for cross tracks and higher convergence rate of the accurate rate of association in the case of near-range parallel tracks than K-merdoids clustering algorithm.