【摘要】 提出一种基于级联特征提取和核模糊聚类的雷达辐射源信号自动分类方法.该方法利用改进的瞬时自相关提取信号的瞬时频率,采用二次归一化处理的特征再提取方法提取分类特征向量,利用具备有效性评价的核模糊聚类算法实现信号的自动分类.仿真结果表明,本文所构建的特征向量能反映不同调制类型信号的变化差异,且具备一定的抗噪能力,在信噪比不低于6 dB情况下,可获得不低于95%的整体分类性能.更多还原

【Abstract】 A new approach of automatic classification of the radar emitter signals is presented.This method is based upon the characteristics derived from instantaneous frequencies and contains three sequential steps: firstly,the instantaneous frequencies of radar emitter signals are extracted via the improved instantaneous autocorrelation algorithm;secondly,a two-times-normalizations based feature re-extraction approach is used to obtain the classification characteristic vector;finally,the kernelized fuzzy c-means clustering with validity assessment is carried out to categorize the considered signals automatically.The results of simulation experiments show that,the characteristic vector can reflect the differences of signals with different modulation types and has a good ability to resist noises.The proposed scheme of signals classification can achieve the overall success rate of above 95%,even at the signal to noise ratio of 6dB.更多还原