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Updated: Jan 16, 2026

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由眼睛模式驱动的GNSS干扰识别特征:ICOA-CNN-ResNet-BiLSTM优化深度学习架构

Chuanyu Wu1,2, Yuanfa Ji1,3, Xiyan Sun1,4

  • 1Information and Communication School, Guilin University of Electronic Technology, Guilin 541004, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个AI框架,使用眼睛图来分类全球导航卫星系统 (GNSS) 的干扰,增强安全性. 这种新的方法在识别各种干扰类型方面取得了很高的准确性.

关键词:
在GNSS中检测干扰.深度学习是一种深度学习.进入的过程中,图像处理是图像处理的过程.进行元启发式优化优化.信号的分类信号的分类.

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科学领域:

  • 信号处理 信号处理
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 全球导航卫星系统 (GNSS) 面临来自信号干扰的重大安全挑战.
  • 现有的干扰检测和分类方法往往缺乏效率和准确性.

研究的目的:

  • 为GNSS干扰类型的智能分类提出一个新的深度学习框架.
  • 通过先进的信号分析,提高GNSS操作的安全性和可靠性.

主要方法:

  • 将GNSS信号转换为2D眼图以进行视觉表示.
  • 利用以为中心的特征分析来进行干扰歧视.
  • 设计用于信号分析的混合深度学习架构 (CNN,ResNet,BiLSTM).
  • 采用改进的coati优化算法 (ICOA) 进行超参数调整.

主要成果:

  • 提出的方法实现了高性能指标:98.02%的准确性,97.09%的精度,97.24%的回忆,97.14%的F1得分和99.65%的特异性.
  • 在各种干扰数据集上,与现有模型相比,表现出显著的改进.
  • 与主流方法相比,ICOA算法在趋同准确度上显示了30%以上的改进.

结论:

  • 开发的基于眼睛图的深度学习框架为GNSS干扰识别提供了高效和准确的解决方案.
  • 意识到的特征提取和混合网络架构是该方法成功的关键.
  • 这项研究提供了一种实际可行的方法,以加强GNSS安全,防止各种干扰威胁.