基于CNN网络的跳频信号个体识别

针对传统跳频信号指纹特征提取只是利用深度学习进行分类的问题,利用CNN网络特征提取的特性,实现了一种基于CNN网络的对预处理后的跳频信号实现特征提取和分类网络模型。首先将收集的跳频信号进行短时傅里叶变换转换到跳频信号敏感的频域,将跳频信号频谱图作为CNN网络模型的输入,CNN网络通过多层卷积提取信号频域深层次特征,通过Batch Normalization、Callback函数的优化加快了网络的收敛速度,同时防止了过拟合现象,最终输出跳频信号的识别分类结果。对比实验结果表明,CNN网络的分类识别正确率较以往的方法更高,在信号信噪比越大的情况下,识别效果越好。

Individual Identification of Frequency Hopping Signals Based on CNN Network

Previous researches of fingerprinting feature extraction of frequency hopping signals have just classified signals through deep learning. We wish to create a new model based on CNN convolutional neural network which can extract the characteristics of the pre-processed frequency hopping signals and classify them using those characteristics. Firstly, we perform short-time Fourier transform on the collected frequency hopping signals to present them in frequency hopping sensitive frequency domain. The converted signals will be putted into the CNN network model and convolved, pooled and fully connected so that we can get final classification results. In this process, we use multi-layer convolution to extract deep level features in the frequency domain of the signals and apply Batch Normalization and Callback functions which can not only optimize and accelerate network convergence speed, but also prevent overfitting effectively. From the final data, the new network model has higher individual recognition accuracy rate than the previous.