CAJ | 학술논문

针对基于密度聚类（DBSCAN）算法不能发现雷达信号密度分布不均匀的缺陷,提出了一种基于近类点和模糊点的聚类方法。该方法利用同一部雷达数据的分布特性进行聚类,通过确定近类点和模糊点以达到分选不同密度分布的雷达信号,适用于未知雷达信号的分选。算法测试表明,该方法对噪声不敏感,能够发现任意形状、大小和密度的聚类。
침대기우밀도취류（DBSCAN）산법불능발현뢰체신호밀도분포불균균적결함,제출료일충기우근류점화모호점적취류방법。해방법이용동일부뢰체수거적분포특성진행취류,통과학정근류점화모호점이체도분선불동밀도분포적뢰체신호,괄용우미지뢰체신호적분선。산법측시표명,해방법대조성불민감,능구발현임의형상、대소화밀도적취류。
Aiming at the problem that the algorithm of density-based spatial clustering of applications with noise（DBSCAN） can not find the radar signal density distribution is not even,this paper presents a new clustering algorithm based on close-category and fuzzy points.This method performs clustering by means of the distribution characteristics of data in the same radar,through confirming close-category points and fuzzy points,it can sort the radar signals of different density distribution,which is adapted to sort unknown radar signals.The algorithm test shows that the proposed method is not sensitive to noise and can find the clustering with arbitrary shapes,size and densities.