LJCD-Net:基于深度对抗转移的跨域干扰泛化诊断网络
Zhichao Zhang1, Zhongliang Deng1, Jingrong Liu1
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
一个新的深度学习网络,LJCD-Net,改善了全球导航卫星系统 (GNSS) 和5G定位的干扰诊断. 它通过解决干扰分类中的域转移问题来提高准确性和信心.
科学领域:
- *用于导航系统的信号处理和机器学习.
- * 在具有挑战性的无线电频率环境中稳固定位.
背景情况:
- * 全球导航卫星系统 (GNSS) 和5G提供了重要的定位,导航和定时 (PNT).
- *5G环境中的低信号功率增加了对干扰的脆弱性,影响了PNT的准确性.
- *现有的干扰诊断方法在适应性和现实世界的分布变化方面扎.
研究的目的:
- * 开发一个强大的干扰诊断网络,可以在不同的数据分布中进行概括.
- * 提高GNSS和5G接收器干扰分类的准确性和可靠性.
- * 克服传统方法和深度学习模型面临的领域差异的局限性.
主要方法:
- * 引入LJCD-Net,一个基于移民的深度对抗性跨领域网络.
- * 使用完全标记的源域和多个未标记的辅助域来进行功能学习.
- * 实施一个以不确定性为导向的辅助域标记权重策略和概率空间约束.
主要成果:
- *LJCD-Net显示了识别准确度和对干扰诊断的信心的提高.
- * 提出的不确定性加权和空间约束方法改善了概括能力.
- *与其他五种诊断方法相比,观察到显著的性能提升.
结论:
- *LJCD-Net有效地解决了干扰诊断中的跨域概括挑战.
- * 该网络为频谱传感和缓解提供了更具适应性和可靠性的解决方案.
- * 这项研究有助于在各种操作环境中更具弹性定位服务.
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