基于人工智能的卫星通信干扰信号识别方法研究

Research on Identification Method of Satellite Communication Jamming Signal Based on Artificial Intelligence

卫星通信系统面临着日益严峻的电磁干扰环境,急需一种高效可靠的干扰信号识别方法。文章提出了一种基于人工智能技术的识别方法,融合了卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆网络(Long Short-Term Memory,LSTM)等深度学习算法,能够自适应地提取干扰信号的时频特征和动态特性,实现高精度、低虚警的实时识别。仿真实验表明,文章所提方法在复杂电磁环境下具有显著的性能优势,对各类干扰信号的平均识别准确率高达97.6%,为卫星通信系统的安全防护提供了有力支撑。

Satellite communication system is facing increasingly severe electromagnetic interference environment,and an efficient and reliable interference signal identification method is urgently needed.In this paper,an identification method based on artificial intelligence technology is proposed,which combines deep learning algorithms such as Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM).It can adaptively extract the time-frequency characteristics and dynamic characteristics of interference signals,and realize real-time identification with high accuracy and low false alarm rate.Simulation results show that the method proposed in this paper has significant performance advantages in complex electromagnetic environment,and the average recognition accuracy of various interference signals is as high as 97.6%,which provides a strong support for the safety protection of satellite communication systems.