在分布式多传感器多目标跟踪系统中，航迹关联是进行航迹融合的必要预处理步骤. 针对传统航迹关联方法在密集目标环境下关联正确率严重下降的问题，文中提出一种新的航迹关联方法. 该方法对传统的模糊隶属度进行时间平滑，并在整体上而不是单个隶属度上考虑航迹的关联程度，从而提出一种修正模糊隶属度来衡量两条航迹的相似程度. 此外还设计了一种基于先验有序关联对的训练方法来设置参数. 仿真结果表明，与经典的模糊双门限方法相比，该方法易于设置参数且性能更好，因而更适合于工程应用.

Track association is a prerequisite to track fusion in distributed multi-sensor/multi-target tracking.With the traditional method, the correct association rate seriously declines in a dense target environment.To solve this problem, this paper presents a new track association method. Traditional fuzzy membership is smoothed, and similarity among tracks is considered from a whole view. Thus a modified fuzzy membership is proposed. In addition, a training method is designed to obtain the parameters based on a priori sequential association couples. Simulation shows that the method can easily set parameters and has better performance as compared to the classical fuzzy double-threshold method, therefore more suitable to practical applications.