Identification of Suspicious Data for Robust Estimation of Stochastic Processes
IX Hotine-Marussi Symposium on Mathematical GeodesyInternational Association of Geodesy Symposia(2019)
摘要
Many geodetic measurements which are automatically gathered by sensors can be interpreted as a time series. For instance, measurements collected by a satellite platform along the satellite’s track can be seen as a time series along the orbit. Special treatment is required if the time series is contaminated by outliers or non-stationarities, summarized as ‘suspicious data’, stemming from sensor noise variations or changes in environment. Furthermore, the collected measurements are often – for instance due to the sensor design – correlated along the track.
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关键词
AR-processes, Hypothesis tests, Outlier detection, Residual time series, Stochastic modeling, Time series
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