中文摘要：

多目标跟踪数据关联时，联合概率数据互联（YPDA）算法是最常用的方法之一，由于其只对最新的确认量测集合进行研究，因此属于次优的贝叶斯算法。为进一步提高JPDA算法的性能，基于最优贝叶斯算法的思想，将包含目标历史信息的目标航向信息引入JPDA算法过程中，即改进算法在计算关联代价时融合目标历史航向信息。仿真结果表明：与改进前JPDA算法相比，本方法有效增加了正确关联概率。

英文摘要：

In multi-target tracking data association problem, Joint Probabilistic Data Association （JPDA） is one of the most popular algorithms, while as only the most recent measurements are useful in JPDA, it is a sub-optimal Bayes algorithm. To improve the performance of JPDA, based on the optimal Bayes algorithm, the improved JPDA algorithm leads into the course information which is based on history information, and fuses the course information with association costs in the improved JPDA algorithm. Simulations show that comparing with the JPDA algorithm, the improved algorithm can increase the correct association probability effectively.