舰船电子对抗

频谱管控旨在减少己方用频设备间自扰、互扰。掌握设备当前的用频情况，是对设备用频或辐射进行动态管控的前提。在实际应用中，往往难以保证用频设备上报信息的准确性，而监测设备采集到的设备信号也会呈现出相对复杂的样式。传统的信号识别方法主要针对固定、常发的信号，对于偶发、时变的信号难以发挥作用。提出了一种基于深度学习的信号识别方法，通过构造信号的时频图像，利用图像分类技术，提取信号动态频谱特征，提升对偶发、时变信号识别的准确率，通过仿真实验，验证了方法的有效性。

Spectrum management and control aims to reduce self-interference and mutual interference between own frequency equipments.To master the current frequency usage of equipment is the premise of implementing dynamic control of equipment frequency usage and radiation.In practical applications, it is often difficult to ensure the accuracy of the information reported through the frequency equipment, and the equipment signals collected by the monitoring equipment will also show a relatively complex style.The traditional signal recognition methods mainly target at the fixed and frequent signals, and it is difficult to play a role in the occasional and time-varying signals.A signal recognition method based on deep learning is proposed.By constructing time-frequency images and using image classification technology, the dynamic spectrum features of signals are extracted to improve the recognition accuracy of occasional and time-varying signals, and the effectiveness of the method are verified through simulation experiments.