基于机器学习的故障检测和排除全球导航卫星系统伪色在测量领域的排除
Ma'mon Saeed Alghananim1, Cheng Feng1, Yuxiang Feng1
1Department of Civil and Environmental Engineering, Imperial College London, Skempton Building, South Kensington, London SW7 2BU, UK.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了全球导航卫星系统 (GNSS) 中基于机器学习 (ML) 的故障检测和排除 (FDE) 的框架. 在KNN模型中,可以达到95%以上的准确度,用于检测4米以上的伪范围测量缺陷.
科学领域:
- 导航系统 导航系统
- 机器学习应用 机器学习应用
- 信号的完整性 信号的完整性
背景情况:
- 全球导航卫星系统 (GNSS) 对于需要高度完整性的关键任务应用至关重要.
- 故障检测和排除 (FDE) 对于减轻故障测量对系统完整性的影响至关重要.
- 在测量领域对基于ML的FDE现有的研究是有限的,缺乏对故障值和全面性能指标的评估.
研究的目的:
- 引入基于ML的传统FDE预测模型在伪色域测量领域的全面框架.
- 为了评估错误检测值的伪范围测量在一个范围的值 (1-40m).
- 基于准确性,错误检测概率,故障检测概率和数据排除的ML模型进行全面评估.
主要方法:
- 使用了六种ML模型:决策树,K-最近邻居 (KNN),歧视,物流,神经网络和树木 (增强,包装,Rusboosted).
- 在40个故障检测值 (1-40m) 中评估了FDE性能.
- 使用准确性,错误检测概率,故障检测概率和排除数据的百分比来评估模型.
主要成果:
- ML模型在FDE中表现出很高的性能,在4米及以上的故障值中达到95%以上的准确性.
- 在故障检测和排除方面,K-Nearest Neighbors (KNN) 的表现最高.
- 该研究提供了 ML 模型的彻底评估,超出了简单的准确度指标.
结论:
- 在GNSS伪范围测量中,ML模型对FDE有效,特别是在复杂的环境中.
- 拟议的框架和评估指标为基于ML的FDE性能提供了更全面的理解.
- 通过强大的FDE,KNN是通过强大的FDE来增强GNSS完整性的有希望的模型.
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