深度学习软决策GNSS多路径检测和缓解
Fernando Nunes1,2, Fernando Sousa1,3
1Instituto de Telecomunicações, 1049-001 Lisboa, Portugal.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究引入了一个卷积神经网络 (CNN),用于检测全球导航卫星信号 (GNSS) 数据中的多路径效应. 使用频域处理显著提高了检测准确性,以改善导航解决方案.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 地理学工程 工程地质学
背景情况:
- 全球导航卫星信号 (GNSS) 信号中的多路径效应降低了定位准确度.
- 现有的多路检测方法在各种信号条件中往往缺乏稳定性.
- 卷积神经网络 (CNN) 显示出复杂信号分析的前景.
研究的目的:
- 开发和验证一种基于CNN的技术,用于检测GNSS信号中的多路径干扰.
- 通过使用频域数据预处理来评估通过使用频域数据预处理实现的性能提升.
- 根据CNN输出提出导航解决方案策略,以减轻多路径效应.
主要方法:
- 一个CNN的设计和训练是使用各种多普勒频率和代码延迟的噪音相关器输出的合成数据集.
- 数据集模拟了基于已建立的多路径模型的多路径干扰信号.
- 在对应器输出的频域预处理中应用了二维离散里埃变换.
主要成果:
- 美国有线电视新闻网 (CNN) 证明了在一系列载体对噪声密度 (C/N0) 值中有效检测多路径效应.
- 与时间域处理相比,在频域中预处理数据显著提高了CNN的检测准确性.
- 两种不同的策略,硬和软决策,被提出用于使用CNN输出解决导航方程.
结论:
- 拟议的CNN技术,特别是频域预处理,为GNSS中多路径检测提供了一个强大的方法.
- 开发的方法可以集成到导航系统中,以拒绝或减轻多路信号的影响.
- 这项研究有助于提高GNSS定位在具有挑战性的环境中的可靠性和准确性.
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