Effect of Surface Mass Loading on Geodetic GPS Observations

We investigated the effect of mass loading (atmospheric, oceanic and hydrological loading (AOH)) on Global Positioning System (GPS) height time series from 30 GPS stations in the Eurasian plate. Wavelet coherence (WTC) was employed to inspect the correlation and the time-variable relative phase between the two signals in the time–frequency domain. The results of the WTC-based semblance analysis indicated that the annual fluctuations in the two signals for most sites are physically related. The phase asynchrony at the annual time scale between GPS heights and AOH displacements indicated that the annual oscillation in GPS heights is due to a combination of mass loading signals and systematic errors (AOH modelling errors, geophysical effects and/or GPS system errors). Moreover, we discuss the impacts of AOH corrections on GPS noise estimation. The results showed that not all sites have an improved velocity uncertainty due to the increased amplitude of noise and/or the decreased spectral index after AOH corrections. Therefore, the posterior mass loading model correction is potentially feasible but not sufficient.

Precision of continuous GPS velocities from statistical analysis of synthetic time series

We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, followed by the presence of offsets and the noise (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain the velocity accuracies (expressed as 95 % confidence limits) based on simple parameters: (1) Series durations over 8.0 years result in high velocity accuracies in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1); (2) Series durations of less than 4.5 years cannot be used for high-precision studies since the horizontal accuracy is insufficient (over 1.0 mm yr−1); (3) Series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal accuracy (0.6 mm yr-1) and a poor vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a better accuracy compare to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

Reduction of GPS Noise for Precision Control of Robot Navigation in Confined Areas

Most modern navigation systems solve the localization problem by extensively using global positioning system (GPS) data. Unfortunately the GPS data quality depends on several factors, which can lead to large positioning errors. Known GPS errors fall in two groups: either atmospheric errors, or multipath errors. Because of these errors, differential GPS systems have been developed using both ground based and satellite based reference systems. The cost of a differential GPS unit such as a Novatel range from a little over $2000 to over $9000, which can be prohibitive for use on certain home service robotic vehicles such as autonomous snow plows or autonomous lawn mowers. This paper discusses ways of mitigating GPS errors on low cost single frequency GPS units such as Copernicus II, Skytraq and U-Blox, which cost far less than $100 each, and hence are attractive for use in many robotic applications such as those mentioned above. The paper will present a model of GPS noise and use that model to process GPS data for use in navigation of an autonomous snow plow designed for use in residential driveways and side-walks; it will be supported by experimental results only.