基于卷积双向长短时记忆网络的雷达辐射源信号识别

Radar Emitter Signal Recognition Based on Convolutional Bidirectional Long-and Short-Term Memory Network

雷达辐射源信号识别在实际战场中是对敌制胜的重要手段。为解决人工提取的雷达辐射源信号特征参数不完备、时效性低等问题,基于模糊函数在表征信号内在结构上的独特作用,提出一种结合模糊函数主脊坐标变换的卷积双向长短时记忆网络的识别方法。首先,为放大不同信号间的差异,采用数学思维将主脊切面转换为极坐标域的几何图像,以此作为神经网络的输入;其次,设计卷积神经网络来挖掘二维时频图的特征信息;最后,搭建双向长短时记忆网络对提取到的特征进行分类识别。仿真实验结果表明,所提方法在信噪比为0 dB以上均能保持100%的准确率,即使信噪比为-6 dB时,识别率仍可达93.58%以上,同时也有效缩短了信号分类时间。结果验证了所提方法不仅能提取信号的隐藏抽象特征,还具备良好的时效性和抗噪性。

Radar emitter signal recognition is an important means to defeat the enemy on the actual battlefield.To solve the problems of incomplete characteristic parameters and low timeliness of an artificial extraction’s radar emitter signal,based on the unique role of the ambiguity function in characterizing the internal structure of the signal,this study proposes a recognition method for convolutional bidirectional long-and short-term memory network combined with the transformation of the main ridge coordinate of the ambiguity function.First,to amplify the difference between different signals,the main ridge section was mathematically converted into a geometric image in the polar coordinate domain,which was used as the input of a neural network.Second,a convolutional neural network was designed to excavate the feature information of a two-dimensional time-frequency map.Finally,a bidirectional long-and short-term memory network was built to classify and recognize the extracted features.Simulation results show that the proposed method maintains 100%accuracy even when the signal-to-noise ratio is above 0 dB,the recognition rate reaches above 93.58%even at-6 dB,and the signal classification time is effectively shortened.Furthermore,the proposed method extracts the hidden abstract features of the signal and produces good timeliness and antinoise performances.