基于EMD和SVD特征提取的通信辐射源个体识别方法

为改善通信辐射源指纹特征提取算法抗噪声及干扰能力差导致的对通信辐射源个体分类识别率低和稳定性差的问题,提出了一种基于经验模态分解和奇异值分解特征提取的方法.通过对信号进行经验模态分解,来克服噪声对指纹特征提取的影响,经希尔伯特-黄变换和奇异值分解实现对通信辐射源信号的指纹特征提取,结合支持向量机算法完成对通信辐射源的个体识别,从而提高了分类识别的正确率,经过对4类辐射源信号的实验验证表明识别效果具有明显提升.

An Identification of Individual Radiation Sources Based on EMD and SVD Feature Extraction

Aimed at the problems that recognition rate is low, and stability of anti jamming and anti noise is poor, with the result that individual classification of communication emitter is poor and interference ability of fingerprint feature extraction algorithm of communication emitter is poor, a method based on empirical mode decomposition and singular value decomposition is proposed. The effect of noise on fingerprint feature extraction is overcome with signal being subjected to the Empirical Mode Decomposition, the fingerprint feature extraction of signal source is realized by the Hilbert Huang Transform and Singular Value Decomposition in combination with Support Vector Machine (SVM) algorithm to complete individual identification of communication source, thus improving the accuracy of the classification and recognition. The experimental verification of the four types of emitter signals show that the ascension of recognition effect is obvious.