一个基于物理的残余和粒子群集优化框架,用于基于物理的无人机GPS伪造检测
1School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一个新的框架,PIR-PSO-XGBoost,有效地检测无人机 (UAV) 中的全球定位系统 (GPS) 伪造. 这种基于物理的方法可以实现高精度,提高无人机导航系统的可靠性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 导航和定位 导航和定位
- 机器学习用于网络安全
背景情况:
- 无人驾驶飞行器 (UAV) 在导航方面严重依赖全球导航卫星系统 (GNSS).
- 全球定位系统 (GPS) 伪造是一个关键的漏洞,危及无人机导航完整性.
- 现有的检测方法往往缺乏对各种伪造场景的解释性或稳定性.
研究的目的:
- 开发一个强大的和可解释的框架来检测针对无人机的GPS伪造攻击.
- 提高无人机导航系统对GNSS信号操纵的可靠性和安全性.
- 克服传统特征工程和黑子深度学习模型在伪造检测方面的局限性.
主要方法:
- 整合了基于物理学的残余 (PIR) 建模与粒子集群优化 (PSO) 和极端梯度提升 (XGBoost) 的集成.
- 物理可解释的残余结构强制执行跨GNSS可观测的时间和载体水平的一致性.
- PSO用于全球超参数调整,以优化分类器通用化和稳定性.
主要成果:
- 在PIR-PSO-XGBoost框架中,在真实世界GPS伪造数据集上,分类准确率达到95.26%,F1得分达到95.28%.
- 与传统机器学习基线相比,表现出显著的性能改善.
- 验证了物理引导特征构建与群体优化学习相结合的有效性.
结论:
- 拟议的PIR-PSO-XGBoost框架为无人机中GPS伪造检测提供了一个强大,高效和可部署的解决方案.
- 将基于物理的特征工程与优化的机器学习相结合,可以增强检测能力.
- 与现有方法相比,这种方法提高了可解释性和计算效率.
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