深度特征选择网络在雷达信号识别中的应用

在现有的雷达辐射源信号识别研究中，传统人工提取到的特征虽具有较为良好的物理表征，但特征中还存在冗余、噪声特征，而通过深度神经网络虽可以挖掘到对信号更深层次的表达，但其特征存在的“黑箱”难以解释性无法避免.结合人工特征良好的物理表征性和深度学习强大的学习能力，本文提出将一种深度特征选择网络（DFS，Deep Feature Selection）应用到雷达信号识别技术中.DFS通过在深度神经网络的输入层和第一隐藏层之间增添一对一层，获取针对每维特征与分类相关性度量得到的权值，以此权值作为衡量标准，加强敏感特征的输入影响，削弱冗余、噪声特征的输入影响，提高分类准确率.方法先对雷达信号提取复杂度特征、小波脊频级联特征、信息熵特征，合并建立原始特征集，利用DFS进行学习训练，以达到在输入级别实现特征选择的目的.本文已利用上述方法对5类辐射源信号进行仿真实验，识别效果良好，验证了方法有效.

Application of Deep Feature Selection Network in Radar Signal Identification

About radar emitter signal identification research, the artificially extracted features have relatively physical characterization, but there are still redundant features and noise features. Through the deep neural network, the deeper expression of the signal can be obtained, but its characteristics are difficult to explain. Combining the physical characteristics of artificial features and the strong learning ability of deep learning, this study proposes to apply a deep feature selection network to radar signal recognition technology. DFS adds a sparse one-to-one layer between the input layer and the first hidden layer to obtain the corresponding weight value of each feature from the classification correlation metric, uses these weight values to enhance the input of sensitive features and weaken the input of redundant features, and improves classification accuracy. Firstly, the complexity features, Cscade Connection features of ridge-frequency, and information entropy features are extracted from the radar signals, and merged into the original feature set. The DFS is used for learning training to achieve the feature selection at the input level. The above approaches were used to identify the 5 different types of radar emitter signals, obtained good classification. The results verify the effectiveness of the approach.