A new fast algorithm is presented to solve the data association in multitarget tracking in cluttered environment.
提出了一种新的多目标跟踪快速数据关联算法。
A new multitarget tracking method based on multidimensional assignment algorithm is developed for passive DOA systems in the paper.
该文针对无源测向交叉定位系统提出了基于多维指派算法的被动多目标跟踪方案。
The target track in the multisensor multitarget tracking fusion contains the fuzzy information which was described by the fuzzy membership function.
多传感器多目标跟踪融合中的目标航迹包含了模糊信息，这种模糊信息可以用模糊隶属度函数来描述。
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 computer simulating shows that the mathematical representation described in this paper is very suitable for optimizing the processing of multitarget tracking.
计算机仿真表明，文中给出的基本原理与数学表示，适于作多目标跟踪优化处理的基础。
So a new multitarget tracking algorithm is proposed, which combines the particle filter and the Gibbs sampler, and can track maneuvering multitarget in cluttered environment very.
提出了一种结合粒子滤波器和吉布斯采样器的多机动目标跟踪算法，可以很好地解决在杂波环境下的多机动目标跟踪问题。
Simulation results show the algorithm can effectively solve the problem in multitarget tracking, and has a higher accurate rate of association. Besides, the CPU occupies a relatively short time.
仿真结果表明，该算法能够有效解决多目标跟踪中的数据关联问题，并且有较高的关联正确率，而且CPU占用时间较短。
Under the global optimal association cost frame, fusion of above two matches is realized to improve the accuracy of tracking, which can resolve the complex multitarget tracking problem availably.
最后在关联代价全局最优化框架下，将两者匹配结果融合，以提高多目标跟踪的正确率。
So tracking multitarget with maneuverability in clutter has to tackle the maneuvering of multitarget as well as the association between measurements and targets.
因此，杂波环境下的多机动目标跟踪问题需要同时解决多个目标的机动性以及测量和目标之间的关联问题。
So tracking multitarget with maneuverability in clutter has to tackle the maneuvering of multitarget as well as the association between measurements and targets.
因此，杂波环境下的多机动目标跟踪问题需要同时解决多个目标的机动性以及测量和目标之间的关联问题。
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