| 引用本文: | 陈利虎,张尔扬,沈荣骏.基于优化初始聚类中心K-Means算法的跳频信号分选.[J].国防科技大学学报,2009,31(2):70-75.[点击复制] |
| CHEN Lihu,ZHANG Eryang,SHEN Rongjun.The Sorting of Frequency Hopping Signals Based on K-Means Algorithm with Optimal Initial Clustering Centers[J].Journal of National University of Defense Technology,2009,31(2):70-75[点击复制] |
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| 基于优化初始聚类中心K-Means算法的跳频信号分选 |
| 陈利虎1, 张尔扬1, 沈荣骏2 |
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(1.国防科技大学 电子科学与工程学院，湖南 长沙 410073;2.解放军总装备部 科学技术委员会，北京 100080)
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| 摘要: |
| 提出了一种优化初始聚类中心的方法。方法通过搜索参数统计直方图峰值预估类数目，并根据峰值位置确定聚类中心大概位置。由于优化的初始类心与实际类心相隔不远，聚类迭代次数大为减少。与传统的优化聚类中心方法相比，本方法计算量更少。最后将改进K-Means聚类算法应用于跳频信号分选，仿真结果表明，分选效果良好。 |
| 关键词: 聚类 K-Means算法 跳频 信号分选 |
| DOI： |
| 投稿日期：2008-09-19 |
| 基金项目: |
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| The Sorting of Frequency Hopping Signals Based on K-Means Algorithm with Optimal Initial Clustering Centers |
| CHEN Lihu1, ZHANG Eryang1, SHEN Rongjun2 |
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(1.College of Electronic Science and Engineering, National Univ. of Defense Technology, Changsha 410073, China;2.General Equipment Department of PLA, Beijing 100080, China)
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| Abstract: |
| A new method is proposed to select optimal initial cluster centers. By searching parameters' histogram peak values, the number of cluster centers can be estimated, and these optimal initial cluster centers are selected in the columns or cells where the histogram peaks exist. Because these optimal initial cluster centers are near to real cluster centers, the iterations of clustering are reduced efficiently. Theoretical analysis demonstrates that the compute complexity of new method is lower than some conventional techniques. The improved K-Means algorithm is applied to sort frequency-hopping signals, and the simulation results demonstrate that the algorithm is effective. |
| Keywords: clustering K-Means algorithm frequency hopping signal sorting |
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