基于PID和深度卷积神经网络的辐射源识别方法

利用神经网络进行辐射源个体识别时，训练样本的单一性会导致深度网络出现过拟合的现象，继而影响辐射源个体识别的精确性。针对该问题，本文提出一种基于PID算法的深度卷积网络结构，该结构通过在传统卷积神经网络的输出层与输入层间构建一条反馈回路，采用PID算法将网络输出错误率转化为划分训练集数据构成的概率，通过优化训练集数据构成，达到抑制过拟合的目的。将该方法应用于超短波电台识别，平均识别率达到92.59%，识别率方差约为传统算法的1/3，训练用时减少约35 min，上述指标均优于传统神经网络。实验结果表明，该算法增强了深度网络的鲁棒性，有效地抑制了过拟合现象。

Specific Emitter Identification Based on PID and Deep Convolutional Neural Network

With the singleness of the training sample, the phenomenon of overfitting occurs in the deep neural network when used for specific emitter identification (SEI), which in turn affects the accuracy. In this paper, a deep convolutional neural network (CNN) structure based on PID algorithm is proposed to alleviate the problem. The structure builds a feedback loop between the output layer and the input layer of the traditional CNN, transforms the error rate of output layer into the probability of dividing the training set data by using PID algorithm, and inhibits the overfitting by optimizing the composition of training set data. The average recognition rate of the network reaches 92.59%when the method is applied to the recognition of ultrashort wave radio. The variance of the recognition rate is about 1/3 of that of the traditional algorithm, and the training time is reduced by about 35 min, obviously the performance of this method is better than that of the traditional neural network. Experimental results show that the algorithm can enhance the robustness of the deep network and effectively suppress the overfitting phenomenon.