使用卷积神经网络来检测GNSS多路径
Anthony Guillard1,2, Paul Thevenon1, Carl Milner1
1Ecole Nationale de l'Aviation Civile (ENAC), SIGNAV, Toulouse, France.
Frontiers in robotics and AI
|May 30, 2023
概括
本研究引入了一种使用卷积神经网络 (CNN) 来检测全球导航卫星系统 (GNSS) 多路径错误的新方法,仅从相关器输出中检测. 该方法有效地识别高精度的利略和GPS信号的多路径,减少对外部传感器的依赖.
科学领域:
- 卫星导航系统 卫星导航系统
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 多路径干扰是全球导航卫星系统 (GNSS) 精度的一个重大挑战.
- 目前用于多路检测的方法通常需要复杂的外部传感器集成.
- 预测和建模GNSS多路径错误仍然是一个困难的问题.
研究的目的:
- 开发一种仅使用GNSS相关器输出检测大振幅GNSS多路径的方法.
- 利用卷积神经网络 (CNN) 在没有外部传感器的情况下进行多路径检测.
- 评估CNN模型在利略E1-B和GPS L1 C/A信号上的性能.
主要方法:
- 一个卷积神经网络 (CNN) 使用101个相关器输出被训练.
- 代表关联器输出值 (延迟与时间) 的图像被生成为CNN输入.
- 该模型在利略E1-B和GPS L1 C/A信号上进行了评估.
主要成果:
- CNN模型获得了高F分:94.7%的利略E1-B和91.6%的GPSL1C/A.
- 通过将对应器输出和采样频率降低4的因素来减少计算负载,保持了高性能.
- 简化模型在利略E1-B上获得了91.8%的F分,在GPS L1 C/A上获得了90.5%.
结论:
- 一种基于CNN的方法有效地检测到GNSS多路径,仅使用关联器输出.
- 这种方法通过消除对外部传感器的需求,简化了多路径检测.
- 该模型表现出强度和效率,即使计算资源减少.
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