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Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
(American Chemical Society, 2017-07)
A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time ...
Mitigating systematic errors for single frequency GPS receivers employing a penalized least squares methodology
(2006-12-27)
Among the positioning systems that compose GNSS (Global Navigation Satellite System), GPS has the capability of providing low, medium and high precision positioning data. However, GPS observables may be subject to many ...
Mitigating systematic errors for single frequency GPS receivers employing a penalized least squares methodology
(2006-12-27)
Among the positioning systems that compose GNSS (Global Navigation Satellite System), GPS has the capability of providing low, medium and high precision positioning data. However, GPS observables may be subject to many ...
Modifying the stochastic model to mitigate GPS systematic errors in relative positioning
(2007-12-01)
The GPS observables are subject to several errors. Among them, the systematic ones have great impact, because they degrade the accuracy of the accomplished positioning. These errors are those related, mainly, to GPS ...
Using cubic splines to mitigate systematic errors in GPS relative positioning
(2004-12-01)
Systematic errors can have a significant effect on GPS observable. In medium and long baselines the major systematic error source are the ionosphere and troposphere refraction and the GPS satellites orbit errors. But, in ...
Using cubic splines to mitigate systematic errors in GPS relative positioning
(2004-12-01)
Systematic errors can have a significant effect on GPS observable. In medium and long baselines the major systematic error source are the ionosphere and troposphere refraction and the GPS satellites orbit errors. But, in ...
A Robust Methodology for the Sensor Fault Detection and Classification of Systematic Observation Errors
(Elsevier Science, 2017-07)
Robust Data Reconciliation enhances the quality of variable estimates when the data set contains a moderate proportion of atypical observations. But if systematic errors that persist in time, i.e. biases and drifts, are ...