论文标题
根据基于特征融合的卷积神经网络对雷达信号的脉冲内调制分类
Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks
论文作者
论文摘要
基于传输的脉冲对雷达的检测和分类是电子战系统中的重要应用。在这项工作中，我们提出了一种新型的基于深度学习的技术，该技术会自动识别脉冲内调制类型的雷达信号。重新分配了测得的雷达信号的频谱图和检测到由特殊功能过滤的瞬时相位的离群值，用于训练多个卷积神经网络。从网络中自动提取的特征融合在一起，以区分频率和相位调制信号。仿真结果表明，所提出的FF-CNN（基于特征融合的卷积神经网络）技术优于当前的最新替代方案，并且在广泛的调制类型中易于扩展。
Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.
