摘要
为解决雷达站观测数据的分类问题,提出一种改进的基于密度的航迹聚类算法。采用加权Manhattan距离与惩罚系数相结合的距离度量,根据目标运动的特征自定义点的邻域,利用时间裁剪提高算法运行效率。实验结果表明,该算法能高效准确地对数据进行聚类,形成运动目标的航迹。
In order to classify the data of radar,this paper proposes an improved track clustering algorithm based on density.Considering concrete application,the algorithm adopts the distance measure by Manhattan distance and penalty coefficient,newly defined the definition of neighborhood using the character of moving object,and improves the efficiency by time clipping.Experimental result shows that the improved algorithm can cluster the observation data accurately and form the tracks efficiently.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第9期270-272,共3页
Computer Engineering
关键词
聚类
航迹
密度
邻域
时间序列
clustering
track
density
neighborhood
time series
