基于多学习单元卷积神经网络的雷达辐射源信号识别

现有基于人工提取特征的复杂体制雷达辐射源信号识别方法时效性低,识别准确率不佳. 为此,提出了一种基于多学习单元卷积神经网络的识别方法. 首先对辐射源信号的模糊函数进行高斯平滑,以校正噪声带来的毛刺与畸变;然后提取其正交切片作为进一步的特征提取对象;最后构建多学习单元卷积神经网络,学习和提取正交切片深层、泛在的特征,并通过softmax分类器进行分类识别. 仿真实验结果表明,所提方法在信噪比为-2 dB时对6类典型雷达信号的整体平均识别率均保持在99.86%以上,即便是在-6 dB环境中,雷达信号的识别率也可达到88.50%,在极低信噪比条件下具有良好的性能和可行性.

A Recognition Method for Radar Emitter Signals Based on Convolutional Neural Network with Multiple Learning Units

Existing radar emitter signal recognition methods based on manually extract features have problems including low timeliness and poor recognition rate. To address these issues, a new recognition method based on a convolutional neural network with multiple learning units is proposed. First, the burr and distortion caused by noise of ambiguity function of emitter signals are corrected through the Gaussian smoothing. Then, the orthogonal slice is extracted as the further feature extraction objects. Finally, a convolutional neural network with multiple learning units is built to learn and extract the deep and ubiquitous features of the orthogonal slice, which are further classified through the softmax classifier. Simulation results show that the overall average recognition rate of six typical radar signals are all above 99.86% when the signal-to-noise ratio is -2 dB. The recognition rate can reach up to 88.50% when the signal-to-noise ratio is -6 dB. The results prove the good performance and feasibility of the proposed method when signal-to-noise ratiois extremely low.