摘要
在P-PHD滤波多目标状态提取中,传统的K-Means聚类方法存在需要提取峰值、聚类时间长、类簇边缘易被侵蚀等问题。针对此问题,在对一般聚类算法的研究的基础上,进一步提出了一种基于K近邻隶属度P-PHD滤波多目标状态提取算法。该算法首先通过量测与粒子的关联性,根据距离来进行量测筛选剔除虚警量测信息,估计真实目标量测类别,然后利用K近邻隶属度将粒子分配给各个估计的真实量测类别,重新分配粒子集,在新粒子集直接提取目标状态信息,从而避免粒子峰值提取过程,降低了算法的时间复杂度。仿真实验表明,所提算法与传统P-PHD滤波以及其它改进聚类算法的P-PHD滤波相比,具有状态提取精度高以及运算时间短的优点。
Aimed at the problems that in extracting multiple-target state by P-PHD filtering,the traditional K-Means clustering method exists in peak extraction,extended clustering time and incorrect clustering for clusters with different sizes,a new measurement extraction method is proposed based on K-neighborhood membership degree.In the category of measurement,estimation of a target is interrelated with the measurements and the particles,and the distance is used to discard false alarm measurements.The particle distributes to every actual measurement category of each estimation by K-neighboring membership degree.On this basis,a new particle set is formulated,and target state can be extracted directly from the set,and there is no need to execute the peak extraction operation.The simulation results show that the proposed method is high in stable retrieval precision,and short in operation time compared with the K-means method and free clustering method.
作者
王雪
李鸿艳
童骞
蒲磊
WANG Xue LI Hongyan TONG Qian PU Lei(Information and Navigation College, Air Force Engineering University, Xi＇an 710077, China)
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2016年第5期65-69,共5页
Journal of Air Force Engineering University(Natural Science Edition)
基金
陕西省自然科学基金(2015JM6332)
关键词
K近邻隶属度
量测筛选
P-PHD滤波算法
状态提取
K-neighboring membership degree
measurement filtering
P-PHD filtering algorithm
state extraction
