摘要
在噪声环境下,存在扩展目标数未知且变化的多扩展目标跟踪量测集难以划分、计算代价高的问题。为此,提出一种基于均值漂移聚类的量测集划分算法。通过迭代更新中心点,使其收敛于局部最优,并引入极大似然估计技术估计每个划分子集中的目标数,对于目标数大于1的子集采用模糊C均值聚类算法进行二次划分,使得划分的量测子集与各个扩展目标一一对应。实验结果表明,该算法在多扩展目标量测集划分性能上明显优于传统的距离划分和K-means++划分算法,尤其是在保持跟踪精度的前提下量测集划分数和计算代价明显降低,且能较好地划分紧邻扩展目标的量测集。
Taking into account the difficulties of measurement set partition of the multiple extended target due to the unknown target number and the disturbance of the clutter. A novel measurement partition algorithm based on the mean shift clustering is proposed. The local optimum is obtained by iterating update the center point. The maximum likelihood estimation technique is introduced to estimate the number of targets for each cell,if the number is larger than one. It splits the cell into small cells by Fuzzy C-Mean（FCM） clustering algorithm until the cell corresponding to target number. Experimental results show that the proposed algorithm improves the performance of multiple Extended Target Tracking （ETT） compared with distance partition and K-means＋＋ partition,especially effectively reduces partition number and computational cost without losing tracking accuracy,and has a good performance for spatially close targets measurement partition.
出处
《计算机工程》
CAS
CSCD
2014年第12期182-187,194,共7页
Computer Engineering
基金
国家自然科学基金资助项目(61305017)
江苏省自然科学基金资助项目(20130154)
关键词
多扩展目标跟踪
量测集划分
均值漂移聚类
极大似然估计
距离划分
紧邻的扩展目标
multiple Extended Target Tracking （ETT）
measurement set partition
mean shift clustering
maximum likelihood estimation
distance partition
spatially close extended target
