基于加权K-近邻法和SVC的雷达辐射源信号识别

Radar emitter signal recognition based on weighted K-nearest neighbor and SVC

为提高支持向量聚类法对分布复杂、不均匀雷达辐射源信号样本聚类的正确率,提出一种结合剪辑近邻法、K-近邻法和支持向量聚类的无监督分类新方法。先采用支持向量聚类对所有未知样本作预分类,再按照一定的剪辑规则剪掉错误类别,最后利用K-近邻法对剪掉的样本按各已知类别不同分布进行加权分类。IRIS数据和辐射源信号聚类实验结果表明,此方法能平衡数据样本各局部分布,获得全局最优聚类分配。

To enhance the correct rate that support vector clustering（SVC） processes radar emitter signal samples with complex and uneven distributions,a novel unsupervised clustering method combining editing nearest-neighbor,K-nearest neighbor with SVC is presented.SVC is first employed to cluster unknown samples.Then wrong clusters are edited by using editing rules.Finally a K-nearest neighbor is introduced to classify the edited samples in terms of different distributions of known classes in a weighted way.Experiments conducted on IRIS data and radar emitter signals show that the proposed method can balance local distributions of samples and obtain the best global clustering.