摘要
为降低杂波对量测集划分的影响,提出了一种基于快速密度峰值聚类的多扩展目标跟踪算法.首先分析不同分区对跟踪结果的影响,得到最"信息"分区的形式,然后利用快速密度峰值聚类(FDPC)算法对量测集进行划分,减少杂波对量测集划分的影响.实验结果表明,所提算法能够有效抑制杂波的影响,在保证跟踪性能损失不大的情况下大大提高了算法的计算效率,具有较好的应用价值.
In order to reduce the influence of clutter on the measurement set division,a multi extension target tracking algorithm based on fast density peak clustering is proposed in this paper.Firstly,the influence of different regions on tracking results is analyzed,and the most＂information＂partition form is obtained.Then,the fast density peak clustering（FDPC）algorithm is used to partition the measurement set,so as to reduce the influence of clutter on the set of measurement sets.Experimental results show that the proposed algorithm can effectively suppress the influence of clutter,and greatly improve the computational efficiency of the algorithm without the loss of tracking performance.It has good engineering application value.
作者
姚敏
YAO Min(Department of Information Engineering,Datong Vocational and Technical College of Coal,Datong 037003,Shanxi,China)
出处
《内蒙古师范大学学报（自然科学汉文版）》
CAS
2018年第4期282-286,共5页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
山西省高等学校科技创新项目(20161129)
