Joint Probabilistic Data Association (JPDA) algorithm can resolve the problem of tracking targets in clutter.
概率数据互联(JPDA)算法能很好地解决密集环境下的多目标跟踪问题。
The Nearest Near Joint Probabilistic Data Association(NNJPDA) is not used directly in multi-sensor multi-target tracking.
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 common data association algorithms include nearest neighbor algorithm, probabilistic data association and joint probabilistic data association.
常用的数据互联方式包括最远邻数据联解闭解、概率数据互联和解开概率数据互联。
A time and space joint probabilistic data association algorithm is developed to solve the difficult problem of passive multisensor-multitarget tracking.
还提出了一种适合于实际工程应用的时空联合数据概率关摘要联算法，该算法解决了无源多传感器多目标跟踪的难题。
The probabilistic data association algorithm is applied in the spatial domain multi resolution frame and target tracking is implemented at the coarse resolution level.
这个算法在空间多分辨率框架下应用概率数据互联算法，在粗分辨率上实现模糊目标跟踪。
The properties of the joint probabilistic data association(JPDA)are analyzed, and data association is reduced to a sort of constraint combinatorial optimization problem.
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 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.
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.
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。
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