一种基于卷积神经网络的雷达干扰识别算法

干扰识别是雷达抗干扰的前提，但是基于特征参数的识别方法受噪声影响大，且参数的特征提取只是发生在某一脉冲重复周期内，难以识别一些具有时序关系的干扰信号.然而利用特征去识别干扰的思路是可行的，据此，本文提出一种利用两个卷积神经网络级联的干扰类型判别方法，此方法基于信号的伪Wigner-Ville分布，分别利用单周期时频图像完成干扰预分类，多周期合成时频图像完成干扰细分类，实现了8种典型干扰样式的识别，尤其适用于拖引干扰的识别.实验结果表明，在本文生成的数据集上，8种干扰的平均识别正确率达到了98%以上. 

A Radar Jamming Recognition Algorithm Based on Convolutional Neural Network

Jamming recognition is the premise of radar anti-jamming, but the recognition method based on characteristic parameters is greatly affected by noise. In addition, the feature extraction of parameters only can take place in a certain pulse repetition time, so it is difficult to identify some jamming signals with temporal relationship. However, the idea of using features to identify interference is feasible. On this basis, a jamming identification method was proposed, taking a cascade form to join two convolutional neural networks. Based on the Pseudo Wigner-Ville distribution of the signal, this method was arranged to use the single-period time-frequency image to complete jamming pre-classification and the multi-period composite time-frequency image to complete jamming fine classification, and to recognize eight typical jamming types, especially suitable for the pulling off jamming recognition. The experiment results show that the average recognition accuracy of eight kinds of jamming can reach up to 98% on the data sets generated in this paper.