雷达在现代电子战中发挥着至关重要的作用,随着电子对抗与电子反对抗的较量愈演愈烈,复杂电磁环境下,雷达有源干扰的人工提取特征困难以及低干噪比下的识别率较低的问题亟需解决.针对该问题,文中提出了一种基于多尺度全卷积网络(Multiscale and Fully Convolutional Neural Network,MFCN)和门控循环网络(Gated Recurrent Unit,GRU)并联的干扰识别算法.这是一种端对端的深度神经网络模型,其输入干扰信号的原始时域序列,不需要对数据进行复杂的预处理,即可提取信号在时间和空间上的融合特征,并能对不同干噪比下的干扰信号进行分类识别.仿真结果表明,随着干噪比的逐渐增加,网络的识别准确率也逐渐提升;在-10～10dB的全干噪比范围内,网络的整体识别率为99.4％,干噪比为-6dB以上时识别准确率接近100％,与单纯的多尺度全卷积网络、门控循环网络和其他经典模型相比具有更高的识别准确率,且能够适应的干噪比的下限更低.
Radar Active Jamming Recognition Based on Multiscale Fully Convolutional Neural Network and GRU
Radar plays a vital role in modern electronic warfare,and as the contest between electronic countermeasures and elec-tronic resistance intensifies,the problem of difficult manual extraction of features for active radar interference and low recognition rates under low JNR in complex electromagnetic environments needs to be addressed urgently.This paper proposes an interfe-rence recognition algorithm based on the parallelization of multiscale and fully convolutional neural network(MFCN)and gated recurrent unit(GRU)to solve the above problem.This is an end-to-end deep neural network model,which does not require com-plex pre-processing of the data,and the original time domain sequence of the interference signal is input to classify and identify the interference signal under different JNR.Simulation results show that the recognition accuracy of the network gradually increa-ses as the JNR gradually increases;the overall recognition rate of the network is 99.4％in the full JNR range of-10 to 10dB,and the recognition accuracy is close to 100％when the JNR is above-6 dB,which has a higher recognition accuracy compared with the simple multiscale and fully convolutional neural network,gated recurrent unit and other classical models,and the limit of the adaptive JNR is lower.
Radar active jammingInterference recognitionTime domain sequenceDeep learningFeature fusion
万方数据
维普
