基于最大熵直觉模糊核聚类的目标跟踪算法

为了提高杂波环境下目标跟踪的正确关联率和实 时性,本文提出一种基于最大熵直觉 模糊核聚类的目标融合跟踪算法。先通过密度函数法确定初始聚类中心,再通过加入核函数 和放松对隶属度的限制,并且通过样本加权给离群点和样本点不同的权值,从而可以减少离 群点和噪声点的干扰,最后通过直觉指数引入直觉模糊集,得到改进后隶属度矩阵,以隶属 度矩阵作为关联概率进行目标与观测的关联,并用卡尔曼滤波进行目标模型的更新,提高目 标跟踪的正确关联率和实时性。实验表明,本文算法相对传统的模糊C均值聚类算法可以提 高目标正确关联率3%左右,并且在算法耗时方面平均减少了0.1 s,相 对于最大熵模糊C均值 聚类算法可以提高目标正确关联率1.9%左右,算法耗时平均减少0.4 s,表明本文算法在提 高目标跟踪正确关联率并增加算法实时性拥有更好的效果。

Target tracking based on maximum entropy intuitionistic fuzzy Kernel clustering

In order to improve the correct correlation rate and real-time performance of t arget tracking in clutter environment,this paper proposes a target fusion tracking al gorithm based on maximum entropy intuitionistic fuzzy kernel clustering.Firstly,the initial clu stering center is determined by density function method;secondly,the kernel function is added an d the restriction on membership degree is relaxed;finally,the interference of outliers and noise points can be reduced by weighting samples;the intuitionistic fuzzy set is introduced by intu itionistic index to obtain the improved membership degree matrix;finally,the membership degree mat rix is used as the correlation probability to associate the target with the observation;finall y,the Kalman filter is used to update the target model to improve the correct correlation rate and real -time performance of target tracking.Experiments show that the proposed algorithm can improve the correct correlation rate of targets by about 3% and reduce the average time con sumption by 0.1s compared with the traditional fuzzy C-means clustering algorithm.Compared with the maximum entropy fuzzy C-means clustering algorithm,it can improve the correct correlat ion rate of targets by about 1.9% and reduce the average time consumption by 0.4s,which shows that the proposed algorithm has better effect in improving the correct correlation ra te of target tracking and increasingthe real-time performance ofthe algorithm.