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基于DBSCAN的后处理多路径/NLoS偏差估计方法

Yihan Guo¹, Simone Zocca¹, Paolo Dabove²

¹Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.

Sensors (Basel, Switzerland)

|April 27, 2024

概括

本研究引入了一种新的方法,使用DBSCAN来估计全球导航卫星系统 (GNSS) 由城市多路径和非视线 (NLoS) 效应引起的伪范围偏差,从而提高定位精度.

科学领域:

* 卫星导航系统卫星导航系统
* * 信号处理信号处理
* 地质工程工程学

背景情况:

* 城市环境对全球导航卫星系统 (GNSS) 构成重大挑战,原因是多路径和非视线 (NLoS) 效应.
* 这些效应引入伪色偏差,降低了GNSS定位应用程序的准确性.
*目前用于识别和分类多路径/NLoS事件的方法仍然具有挑战性.

研究的目的:

* 提出一种后处理方法来估计由多路径/NLoS效应引起的伪偏差.
*为训练机器学习模型提供准确的伪偏差数据,用于多路径/NLoS检测和缓解.
*为评估旨在检测多路径/NLoS效应的新方法建立一个基准.

主要方法:

*使用基于密度的应用程序与噪声 (DBSCAN) 算法从伪范围测量中提取多路径/NLoS偏差.
*在静态和动态城市多路径/NLoS场景中使用现实数据收集方法的验证.
* 基于定位准确度的性能评估,通过比较从偏差纠正的伪子获得的解决方案与地面真相.

主要成果:

*成功估计了归因于多路径/NLoS条件的伪色偏差.
* 在静态和动态场景中证明了拟议方法的有效性.
*在应用估计偏差时,通过改进定位性能进行验证.

结论:

* 提出的基于DBSCAN的方法有效地估计了城市GNSS环境中的伪色偏差.
* 估计的偏差可以显著提高机器学习算法的训练,以改善GNSS定位.
* 这种方法为多路径/NLoS检测技术提供了可靠的验证策略.

关键词:

集群算法集群算法集群算法集群算法集群算法集群算法多路径的多路径.没有视线的视线.伪色偏见是一种偏见.

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