Jamming Pattern Recognition Using Spectrum Waterfall: A Deep Learning Method

Yuan Cai, Kai Shi, Fei Song, Yifan Xu, Ximing Wang, Heyu Luan
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引用次数: 14

Abstract

It is very important for communication equipment to realize the recognition and prediction of jamming signals in a short time. In the communication environment with variable jamming mode and noise, it is very difficult to achieve rapidly jamming recognition. To solve this problem, this paper constructs a spectrum waterfall containing white gaussian noise signals, designs a jamming pattern recognition method based on deep learning, and realizes rapid recognition of jamming patterns and important parameters. Simulation results show that the training process of this method has considerable accuracy and the test process has high recognition rate.
基于频谱瀑布的干扰模式识别:一种深度学习方法
如何在短时间内实现对干扰信号的识别和预测,对通信设备来说是非常重要的。在干扰方式和噪声多变的通信环境中,实现快速的干扰识别是非常困难的。针对这一问题,本文构建了一个包含高斯白噪声信号的频谱瀑布,设计了一种基于深度学习的干扰模式识别方法,实现了干扰模式和重要参数的快速识别。仿真结果表明,该方法的训练过程具有较高的准确率,测试过程具有较高的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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