首页> 外文会议>ASME joint rail conference >THE APPLICATION OF THE TIME SERIES THEORY TO PROCESSING DATA FROM THE SBAS RECEIVER IN SAFETY MODE
【24h】

THE APPLICATION OF THE TIME SERIES THEORY TO PROCESSING DATA FROM THE SBAS RECEIVER IN SAFETY MODE

机译:时间序列理论在安全模式下处理SBAS接收器数据的应用

获取原文

摘要

Before satellite-based augmentation systems (SBAS) such as the Wide Area Augmentation System (WAAS) in the USA, and the European Geostationary Navigation Overlay Service (EGNOS), will be used in railway safety-related applications, it is necessary to determine reliability attributes of these systems as quality measures from the user's point of view. It is necessary to find new methods of processing data from the SBAS system in accordance with strict railway standards.For this purposes data from the SBAS receiver with the Safety of Life Service was processed by means of the time series theory. At first, a basic statistic exploration analysis by means of histograms and boxplot graphs was done. Then correlation analysis by autocorrelation (ACF), and partial autocorrelation functions (PACF), was done. Statistical tests for the confirmation of non-stationarity, and conditional heteroscedasticity of time series were done. Engle's ARCH test confirmed that conditional heteroscedasticity is contained. ARMA/GARCH models were constructed, and their residuals were analyzed.Autocorrelation functions and statistical tests of models residuals were done. The analysis implies that the models well cover the variance volatility of investigated time series and so it is possible to use the ARMA/GARCH models for the modeling of SBAS receiver outputs.
机译:在将基于卫星的增强系统(SBAS)(例如美国的广域增强系统(WAAS)和欧洲对地静止导航叠加服务(EGNOS))用于与铁路安全相关的应用之前,必须先确定可靠性从用户的角度来看,这些系统的属性作为质量度量。有必要根据严格的铁路标准找到从SBAS系统处理数据的新方法。为此,通过时间序列理论对来自SBAS接收器和“生命安全服务”的数据进行了处理。首先,通过直方图和箱线图进行了基本的统计探索分析。然后通过自相关(ACF)和部分自相关函数(PACF)进行了相关分析。进行统计测试以确认时间序列的非平稳性和条件异方差性。恩格尔的ARCH测试证实了条件异方差性。构建了ARMA / GARCH模型,并对它们的残差进行了分析,并对模型残差进行了自相关函数和统计检验。分析表明,这些模型很好地覆盖了所研究时间序列的方差波动性,因此可以将ARMA / GARCH模型用于SBAS接收器输出的建模。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号