一种基于多频带能量算子的FSK信号分类新方法

A New Classification Method of FSK Signals Using Multiband Energy Operators

本文对MFSK通信信号的分类问题进行了研究。针对信号的特征提取问题,提出了一种稳健的基于多频带能量算子的方法。首先利用小波多频带滤波器组对MFSK信号进行预处理,以减弱噪声对信号的影响,然后对其中的最大频带输出值应用能量算子来提取分类特征;针对无监督聚类算法中FCM算法对初始值敏感,易收敛至局部最优解的缺点,提出了一种基于核的模糊C均值聚类(FKCM)算法来设计分类器,它通过Mercer核把输入数据非线性映射到高维空间,使得在输入空间中线性不可分的样本可分,大大提高了FCM算法的聚类性能。通过计算机仿真可知:多频带能量算子的特征提取方法可以有效地抑制噪声的影响,而FKCM可以更好地进行聚类,其识别精度更高。

In this paper,a new classification algorithm of MFSK signal is analyzed.Aiming at the problem of feature extraction,a method based on multiband energy operators is given.This method first filters the signal through a bank of passband filters to reduce the effect of noise,and then applies the energy operators to the largest filter output response to extract the classification character.Aiming at the disadvantage of FCM algorithm which is sensitive to initial values and easily falls into local optimum solution,a novel cluster algorithm based on the kernel method fuzzy kernel C-means clustering algorithm（FKCM） is used;It uses the Mercer kernel mapping the feature vector impliedly to the high dimensional feature space and makes the feature in the input space undistinguishable become discriminable,and improves the clustering performance.Through computer simulation it can be seen that the feature extraction method based on multiband energy operators can restrain noise effectively and FKCM clusters well,so the classification accuracy is high.