在低JNR设置下基于双分支剩余融合网络的复合干扰识别
Wen Lu1, Junbao Li1, Feng Xie1
1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的深度学习方法,用于识别复杂的雷达干扰信号. 这种方法增强了雷达反干扰能力,特别是在具有挑战性的低信号对噪声环境中.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 雷达系统在复杂的电磁环境中面临着先进的干扰技术带来的重大挑战.
- 现有的识别方法与现代干扰信号的多样性,复杂性和低信号噪声比条件作斗争.
研究的目的:
- 开发一种新的深度学习方法,用于准确和强大的识别复杂的雷达干扰信号.
- 改进雷达反干扰能力,应对复杂的威胁.
主要方法:
- 一个双分支深度学习网络,处理时间域和时间频域特征.
- 纳入多分支卷积结构,以增强特征提取.
- 应用有效的特征融合策略来捕捉微妙的干扰模式.
主要成果:
- 拟议的深度学习方法与六种基线方法相比,显示出更高的识别准确性.
- 该方法显示出显著的稳定性,特别是在低干扰噪声比条件下.
- 在识别多样化和复杂的干扰信号方面表现优于现有方法.
结论:
- 这种新的深度学习方法为复杂的雷达干扰信号识别提供了有希望的解决方案.
- 双分支架构和功能融合策略有效地提高了防干扰性能.
- 这种方法对于在有争议的电磁环境中提高雷达系统弹性至关重要.
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