基于卷积神经网络的无线电广播同频干扰检测

Same Frequency Interference Detection in Radio Broadcast Based on Convolutional Neural Network

针对无线电干扰中较为突出的同频干扰问题,将深度学习应用于干扰信号检测,提出一种无线电调频广播同频干扰检测算法。将调频广播数据转化为能体现信号特性的小波变换时频图,并将其作为卷积神经网络(CNN)的训练数据,训练CNN学习信号的时频特征,得到干扰检测模型。实验结果表明,与传统的机器学习算法相比,该算法能更准确地检测出广播信号中是否存在同频干扰信号,其干扰检测准确率达95.0%。

Aiming at the problem of same frequency interference which is rather prominent in radio interference,deep learning is applied to the interference signals detection,and a new algorithm for detecting same frequency interference in frequency modulation broadcast is proposed.Transform the frequency modulation broadcast signals into the time-frequency images of wavelet which can reflect the signal characteristics,and then those images are used as the training data of the Convolutional Neural Network(CNN).After training the CNN to learn the time-frequency features of the signal,the detection model is obtained.Experimental results show that,compared with the traditional machine learning algorithm,the proposed algorithm can detect whether there is same frequency interference signal in the broadcast signal more accurately,and the accuracy can reach 95.0%.