基于最大熵模糊聚类的快速多目标跟踪算法研究
发布时间：2018-11-11 11:44
【摘要】：为了提高杂波环境中多目标跟踪的实时性和精确性,利用最大熵数据模糊聚类方法得到的模糊隶属度表示目标与量测之间的关联概率,同时分析了公共量测对目标的影响,引入影响因子重建互联概率矩阵,结合概率数据关联算法实现多目标的状态估计。该算法避免了对确认矩阵的拆分,解决了联合概率数据关联算法随着目标和回波数目增加而导致的计算量爆炸性增长问题。针对不同杂波密度环境下的临近平行目标和小角度交叉目标的跟踪进行了仿真分析,仿真结果表明:最大熵模糊聚类联合概率数据关联算法是一种有效的快速数据关联算法,在密集杂波环境中跟踪性能依然优于联合概率数据关联算法和经验联合概率数据关联算法,在一定程度上可以避免航迹融合。
[Abstract]:In order to improve the real-time and accuracy of multi-target tracking in clutter environment, the fuzzy membership degree obtained by maximum entropy data fuzzy clustering method is used to express the correlation probability between target and measurement, and the influence of common measurement on target is analyzed. The influence factors are introduced to reconstruct the interconnected probability matrix and the probabilistic data association algorithm is used to realize the multi-objective state estimation. The algorithm avoids the splitting of the confirmation matrix and solves the problem of explosive increase of computation caused by the increase of the number of targets and echoes in the joint probabilistic data association algorithm. The tracking of adjacent parallel targets and small angle cross targets under different clutter density is simulated. The simulation results show that the maximum entropy fuzzy clustering combined probability data association algorithm is an effective and fast data association algorithm. The tracking performance in dense clutter environment is still better than that of joint probabilistic data association algorithm and empirical joint probabilistic data association algorithm, which can avoid track fusion to some extent.
【作者单位】： 西北工业大学航海学院;
【基金】：国家自然科学基金(51179157、51409214、11574250)赞助
【分类号】：TP311.13
本文编号：2324746
[Abstract]:In order to improve the real-time and accuracy of multi-target tracking in clutter environment, the fuzzy membership degree obtained by maximum entropy data fuzzy clustering method is used to express the correlation probability between target and measurement, and the influence of common measurement on target is analyzed. The influence factors are introduced to reconstruct the interconnected probability matrix and the probabilistic data association algorithm is used to realize the multi-objective state estimation. The algorithm avoids the splitting of the confirmation matrix and solves the problem of explosive increase of computation caused by the increase of the number of targets and echoes in the joint probabilistic data association algorithm. The tracking of adjacent parallel targets and small angle cross targets under different clutter density is simulated. The simulation results show that the maximum entropy fuzzy clustering combined probability data association algorithm is an effective and fast data association algorithm. The tracking performance in dense clutter environment is still better than that of joint probabilistic data association algorithm and empirical joint probabilistic data association algorithm, which can avoid track fusion to some extent.
【作者单位】： 西北工业大学航海学院;
【基金】：国家自然科学基金(51179157、51409214、11574250)赞助
【分类号】：TP311.13
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