实时干扰检测使用窗口和混合机器学习模型进行预和警报.
J Sormayli1, M Darvishi1, K Zarrinnegar1
1Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
Scientific reports
|July 9, 2025
概括
本研究介绍了一种先进的机器学习模型,用于检测全球导航卫星系统 (GNSS) 在Ublox-M8T接收器中的干扰. 该解决方案提供高精度,实时干扰检测,超低延迟,可靠的导航系统.
科学领域:
- 导航系统工程 导航系统工程
- 机器学习应用 机器学习应用
- 信号处理 信号处理
背景情况:
- 全球导航卫星系统 (GNSS) 接收器,如Ublox-M8T,容易受到干扰,包括欺骗和抑制干扰.
- 干扰可以降低导航准确性和可靠性,在安全关键的应用中构成风险.
- 现有的检测方法可能缺乏实时对策所需的速度或准确性.
研究的目的:
- 开发和验证一种新的深度学习和机器学习模型,用于检测Ublox-M8T接收器中的欺骗和抑制干扰.
- 实现适合各种导航环境的实时,超低延迟解决方案.
- 通过早期干扰检测和预和警报来提高GNSS系统的可靠性.
主要方法:
- 提出了一个集成XGBoost的新模型,用于实时干扰信号分类.
- 在STM32H743微控制器上实现解决方案,以实现超低延迟.
- 包含一个用于预和预警和早期干扰检测的窗口机制.
- 使用软件定义无线电进行实验,模拟干扰场景.
- 通过特征规范化,相关性分析和特征选择预处理收集的GNSS和干扰数据.
主要成果:
- XGBoost分类器实现了99.97%的检测率和99.94%的精度.
- 获得0.9992的马修斯相关系数,表明分类准确度很高.
- 每个样本的平均预测时间仅为20微秒,这表明它适合实时应用.
- 窗口机制有效地区分了在静态和动态干扰下高可信度和低可信度GNSS数据.
结论:
- 拟议的XGBoost模型具有窗口机制,可提供高度准确和快速的GNSS干扰检测.
- 实施的解决方案确保了系统可靠性和干扰期间的连续导航操作.
- 这种方法提供了对干扰攻击的强有力的防御,提高了GNSS数据的可靠性.
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