通过对多目标联合概率数据关联方法性能特征的分析，将其归结为一类约束组合优化问题。
The properties of the joint probabilistic data association(JPDA)are analyzed, and data association is reduced to a sort of constraint combinatorial optimization problem.
传统的最邻近联合概率数据关联算法（NNJPDA）不能直接用于多传感器对多目标的跟踪。
The Nearest Near Joint Probabilistic Data Association(NNJPDA) is not used directly in multi-sensor multi-target tracking.
联合概率数据关联算法是公认的多目标跟踪中有效的数据关联算法，但它的计算量过大，实时性不好。
The Joint Probabilistic data association algorithm (JPDA) is the accepted effective data association algorithm, but it has high computational load, and it's not a Real-time algorithm.
动态方法利用真实目标的运动规律去除虚假目标，本文介绍了联合概率数据关联(JPDA)在此的应用。
Dynamic methods employ the rule of movement of real target to erase ghosts. An applying of joint probability data association (JPDA) is presented.
动态方法利用真实目标的运动规律去除虚假目标，本文介绍了联合概率数据关联(JPDA)在此的应用。
Dynamic methods employ the rule of movement of real target to erase ghosts. An applying of joint probability data association (JPDA) is presented.
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