基于DAE+CNN辐射源信号识别算法

针对利用卷积神经网络进行辐射源信号识别过程中时间复杂度高的问题进行研究，提出一种基于降噪自编码器和卷积神经网络结合的算法。首先对雷达辐射源信号进行短时傅里叶变换，获取时频图像；然后对图像进行灰度和阈值二值化处理，将处理后的图像向量化操作输入到降噪自编码器中，提取降噪自编码器隐藏层特征数据完成降维处理，再重构成图片矩阵输入到卷积神经网络中，利用常用的softmax分类器进行分类识别。通过仿真表明，添加降噪自编码器降维处理后的模型相比较原模型，时间复杂度大幅度下降，在SNR=-6 dB，识别效果能达到80%以上，与利用传统降维方式性能相比，识别效果明显提高。

Recognition algorithm of emitter signal based on DAE+CNN

In order to solve the problem of high time complexity in the signal recognition process by using convolution neural network, this paper proposed a new algorithm based on denoising auto-encoder and convolution neural network. Firstly, it performed the STFT on the radar emitter signal to obtain the time frequency image. And it used gray and threshold. It put processed image vectorization into the denoising auto-encoder. Then the denoising auto-encoder extracted the feature data. It put the reconstituted image matrix into the convolution neural network and used softmax classifier as classification and recognition. The simulation shows that the model using DAE is better than the original model and the time complexity decreases greatly. The recognition rate can reach 80% under SNR=-6 dB. Besides, compared with the traditional way, the performance of reducing the dimension is better.