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Precise orbit determination is vital for the increasingly vast number of space objects around the Earth. Moreover, accurate orbit prediction of GNSS satellites is essential for many real-time geodetic applications, including real-time navigation. The typical way to obtain accurate orbit predictions is using physics-based orbit propagators. However, the prediction errors accumulate with time because of insufficient modeling of the changing perturbing forces. Motivated by the rapid expansion of computing power and the considerable data volume of satellite orbits available in recent years, we can apply machine learning (ML) and deep learning (DL) algorithms to assess if they can be used to further reduce orbit errors. In this study, we focus on the orbit prediction of GNSS constellations. We investigate the potential of using different ML and DL algorithms for improving the accuracy of the ultra-rapid products from IGS. As ground truth we consider the IGS final products, and the differences between the ultra-rapid and final products are computed and serve as targets for the ML/DL methods. In this context, we combine the advantages of physics-based and data-driven ML/DL methods. Since the major errors of GNSS orbits are expected to be caused by the deficiency of solar radiation pressure models, we consider different related parameters as additional features to implicitly model the solar impact, such as the C0,0 terms of global ionosphere maps. In order to accurately model the effect of solar radiation pressure on the radial, along-track and cross-track components of the satellite orbit system, the geometric relation between the Sun, the satellite and the Earth are also considered. Furthermore, the performances of different ML/DL algorithms are compared and discussed. Due to the temporal characteristics of the problem, certain sequential modeling algorithms, such as Long Short-Term Memory and Gated Recurrent Unit, show superiority. Our approach shows promising results with average improvements of over 40% in 3D RMS within the 24-hours prediction interval of the ultra-rapid products., EGUsphere

High-precision GNSS (Global Navigation Satellite System) positioning can reach an accuracy at the millimeter level, and by collecting these data over years, very small ground movements can be resolved. These data can be used to study geodynamics, like continental drifts, tidal- and non-tidal loading effects, or earthquakes, for example. Here we focus on the sensitivity of GNSS for resolving these different types of movements. We derive minimal detectable displacements for linear drift rates, annual- and semiannual periodic motions, and offsets – these are main parameters of the so-called standard linear trajectory model for GNSS station motions. For our analysis, our data comes from several hundreds of permanent GNSS stations across Europe– the GNSS stations’ coordinates are obtained at a daily sampling rate, with almost 25 years of data available for some stations. Based on cleaned residual GNSS time series we calibrate a “power-law plus white noise” stochastic model for each station. Together with the functional trajectory model we compute the formal errors of the movement parameters based on a least-squares adjustment. Based on these errors we then introduce the statistics to derive minimum detectable displacements for the movement parameters. Our analysis shows that the minimum detectable trends can be as low as few tenth of millimeters per year. The minimum detectable amplitudes at the annual and semiannual periods are at the millimeter level or lower, and the detectable offset is few millimeters on average, too. As expected, the minimum detectable displacements depend strongly on the length of the datasets and on the noise characteristics. Another important parameter is the number of discontinuities and offsets for each station – it impacts the minimum detectable trend. We conclude that such an analysis can be very useful for sensitivity studies in climate change monitoring. Furthermore, the methodology cannot only be applied in the field of GNSS time series analysis, but also to any other time series data in geosciences., EGUsphere

Global Navigation Satellite Systems (GNSS) have been used as a key technology for satellite orbit determination for about 30 years. With the increasing popularity of miniaturized satellites (e.g., CubeSats that are nanosatellites based on standardized 10 cm-sized units) the need for an adapted payload for orbit determination arises. We developed a small-size versatile GNSS payload board using commercial off-the-shelf single-frequency GNSS receivers with extremely small weight (1.6 g), size (12.2 x 16.0 x 2.4 mm3 ) and power consumption (100 mW). The board features two separate antenna connectors and four GNSS receivers – two connected to each antenna. This redundancy lowers the risk of a total payload failure in case one receiver is malfunctioning. Two prototypes of the GNSS positioning board have been successfully launched onboard the Astrocast-01 and -02 3-unit cube satellites with altitudes of 575 and 505 km, respectively. The multiGNSS receivers are capable of tracking the GNSS satellites of the four major systems, i.e., GPS, GLONASS, BeiDou and Galileo. In addition, both satellites are equipped with a small array of three laser retroreflectors enabling orbit validation with Satellite Laser Ranging (SLR). After the two precursor missions, a constellation of 80 satellites is planned, allowing the formation and computation of a highly uniform polyhedron in space with cm-accuracy, relevant for geocenter, reference frame, and GNSS orbit determination. At present, we have continuous receiver PVT solutions available. The real-time onboard orbit determination results indicate that the receivers perform very well on both satellites. The RMS of a daily orbit fitting is, after removing one or the other outlier, at the level of 2-5 meters despite errors caused by the ionosphere and the orbit model. For a few satellite arcs, the recording of GNSS raw phase and code data was enabled, allowing orbit determination in a post-processing mode. This allows a better assessment of the achievable orbit quality and an overall performance estimation. The tests performed so far include the improvement of the orbit quality by eliminating the ionospheric refraction based on a linear combination of phase and code observations, the comparison of various single-system solutions and advances in combining the different tracking systems for orbit determination. In collaboration with the Zimmerwald Observatory in Switzerland a first SLR campaign was conducted that successfully tracked both nanosatellites. The SLR measurements with their high accuracy were then analyzed to validate the orbits of the Astrocast satellites derived from GNSS measurements. We will present details on the payload board, on the results of the orbit determination in real-time and in post-processing mode based on the low-cost single-frequency multi-GNSS receivers onboard the satellites and on the SLR orbit validation., EGUsphere

In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku‐Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low‐frequency component and/or variations in the long‐term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high‐frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies., Journal of Geophysical Research: Solid Earth, ISSN:2169-9313, ISSN:0148-0227, ISSN:2169-9356

Monitoring surface deformation in real-time help at planning and protecting infrastructures and populations, manage sensitive production (i.e. SEVESO-type) and mitigate long-term consequences of modifications implemented. We present RT-SHAKE, an algorithm developed to detect ground motions associated with landslides, sub-surface collapses, subsidences, earthquakes or rock falls. RT-SHAKE detects first transient changes in individual GPS time series before investigating for spatial correlation(s) of observations made at neighbouring GPS sites and eventually issue a motion warning. In order to assess our algorithm on fast (seconds to minute), large (from 1cm to meters) and spatially consistent surface motions, we use the 1Hz GEONET GNSS network data of the Tohoku-Oki MW9.0 2011 as a test scenario. We show the delay of detection of seismic wave arrival by GPS records is of ~10 seconds with respect to an identical analysis based on strong-motion data and this time delay depends on the level of the time-variable noise. Nevertheless, based on the analysis of the GPS network noise level and ground motion stochastic model, we show that RT-SHAKE can narrow the range of earthquake magnitude, by setting a lower threshold of detected earthquakes to MW6.5-7, if associated with a real-time automatic earthquake location system., Geophysical Journal International, 214 (2), ISSN:0956-540X, ISSN:1365-246X

Journal of Geodesy, 89 (7), ISSN:0949-7714, ISSN:1432-1394

Journal of Geodesy, 87 (6), ISSN:0949-7714, ISSN:1432-1394