基于二级CFSFDP的扩展目标量测集划分算法  

Extended Target Measurement Set Partition Algorithm Based on Second Level CFSFDP

作　　者：迟珞珈 冯新喜[1] 蒲磊[1] 曹倬[1] CHI Luojia;FENG Xinxi;PU Lei;CAO Zhuo(Information and Navigation College, Air Force Engineering University, Xi＇ an 710077, China)

机构地区：[1]空军工程大学信息与导航学院,西安710077

出　　处：《计算机工程》2018年第5期309-315,共7页Computer Engineering

基　　金：国家自然科学基金"基于区间分析技术的随机集多扩展目标跟踪关键技术研究"(61571458)

摘　　要：在扩展目标高斯混合概率假设密度滤波中,量测集的划分需要进行大量计算,导致运行效率较低。针对该问题,提出一种新的扩展目标量测集划分算法。采用局部异常因子对杂波进行滤除,将层次聚类与采用密度极点的算法相结合对量测集进行划分。实验结果表明,与距离划分、K-means++划分、DBSCAN划分算法相比,在扩展目标处于交叉和近邻2种情况时,该算法对目标的外形不敏感,在保证扩展目标跟踪性能的同时,减少了计算时间。In an extended target Gaussian-mixture Probability Hypothesis Density（ PHD） filtering,the partition of measurement set requires a lot of calculation,which leads to the decrease of the efficiency of the algorithm. To solve the problem,a new measurement set partitioning algorithm is proposed. Firstly,it uses the Local Outlier Factor（ LOF）technique to remove the clutter,then the hierarchical clustering combining with the new method using density peaks is used to partition the measurement set. Simulation results show that compared with distance partition、K-means ＋ ＋partition、DBSCAN partition,the proposed algorithm is not sensitive to the shape and can ensure the performance of the extended target tracking,as well as reduces the computational time effectively in the case of target crossover and neighborhood.

关 键 词：扩展目标 局部异常因子 强度函数 高斯混合概率密度 量测集划分 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]