CAJ | 학술논문

针对 KPowerMeans 聚类算法多径散射簇的估计过程复杂及聚类结果高度依赖随机初始簇中心的问题，提出了一种改进的多径簇识别算法--WKPowerMeans 算法。首先利用小波变换的尖峰检测技术估计出多径散射簇的数目和初始簇中心的位置，然后以结合了多径功率加权的多径分量距离为准则进行多径簇聚类。仿真结果表明：与KPowerMeans 算法相比，采用所提出的 WKPowerMeans 算法能得到更稳定、准确的聚类结果，而且具有较低的时间复杂度。
침대 KPowerMeans 취류산법다경산사족적고계과정복잡급취류결과고도의뢰수궤초시족중심적문제，제출료일충개진적다경족식별산법--WKPowerMeans 산법。수선이용소파변환적첨봉검측기술고계출다경산사족적수목화초시족중심적위치，연후이결합료다경공솔가권적다경분량거리위준칙진행다경족취류。방진결과표명：여KPowerMeans 산법상비，채용소제출적 WKPowerMeans 산법능득도경은정、준학적취류결과，이차구유교저적시간복잡도。
In order to solve the problems of KPowerMeans multipath cluster recognition algorithm,which has a complex process of multipath scattering cluster estimation and whose clustering result is highly dependent on the ran-dom initial cluster cancroids. An improved algorithm,named WKPowerMeans,is proposed. The peak detection and information entropy methods are combined to develop the framework of automatic cluster identification. The im-proved algorithm not only acquires the number of cluster and initial centroids by using the wavelet transformation, but also adaptively obtains the different weights of the attributes of the multipath component. Simulation results indi-cate that the proposed WKPowerMeans clustering method can produce more robust and more accurate solutions than KPowerMeans method;furthermore it has lower time complexity.