GNSS/INS/Star Tracker Integration for Real-Time On-Board Autonomous Orbit and Attitude Determination in LEO, MEO, GEO and Beyond

In our previous studies, we demonstrated that Global Navigation Satellite System (GNSS) signals can be processed, not only in Low Earth Orbit (LEO), but also in higher earth orbits, up to the Moon. In order to maximize the GNSSbased navigation performance, we implemented an adaptive orbital filter, which fuses the GNSS observations with a model of the spacecraft dynamics, achieving a navigation accuracy of approximately 100 meters, at Moon altitude. In this paper, we take a step forward and we investigate the design of an advanced multisensor solution that, in addition to combining GNSS with an orbital forces model, also adds the integration of an Inertial Navigation System (INS) and of a Star Tracker, in order to provide a versatile, real-time, on-board, autonomous orbit and attitude determination in different space mission scenarios, from LEO to GEO and beyond. First, we describe the designed architecture of the integrated system, then its implementation, and finally we report its achieved navigation performance for different altitudes up to the Moon, showing that the synergistic integration of the different sensors, can overcome their individual drawbacks and provide a better navigation performance than either could achieve individually

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Tropospheric water vapor is one of the most important trace gases of the Earth\u27s climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)$^2$. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021)

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021)

Total refractivity fields from GNSS tropospheric delays reconstructed with collocation methods

Refractivity of the Earth's troposphere is directly reflected in the observations of space geodesy such as Global Navigation Satellite Systems (GNSS) and satellite radars. Therefore, they can provide valuable information about the quantity and spatio-temporal distribution of atmospheric gases. With improvement of GNSS modeling strategies and increasing network density tomographic approaches were developed. In this paper, we present an alternative tomographic approach based on collocation method to estimate the refractivity field from GNSS observables. Collocation adjustment is used to estimate parameters that relate GNSS observables (such as zenith or slant delays) with refractivities. Therefore, the parameters are used to compute the refractivity field. With this approach, for end-to-end simulations, the refractivity field can be reconstructed with an accuracy of 2.5 ppm and 4.5 ppm in terms of mean and standard deviation respectively; these results are comparable to typical tomography results

A collocation framework to retrieve tropospheric delays from a combination of GNSS and InSAR

High spatio-temporal variability of atmospheric water vapor affects microwave signals of Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). A better knowledge of the distribution of water vapor improves both GNSS- and InSAR-derived data products. In this work, we present a collocation framework to combine and retrieve zenith and (relative) slant tropospheric delays. GNSS and InSAR meteorological products are combined aiming at a better retrieval of the atmospheric water vapor. We investigate the combination approach with synthetic and real data acquired in the Alpine region of Switzerland. Based on a closed-loop validation with simulated delays, a few mm accuracy is achieved for the GNSS-InSAR combination in terms of retrieved ZTDs. Furthermore, when real delays are collocated, the combination results are more congruent with InSAR computed products. This research is a contribution to improve the spatio-temporal mapping of tropospheric delays by combining GNSS-derived and InSAR-derived delays

Water Vapor Fields by Collocation of GNSS zenith total delays and InSAR relative slant delays in the Upper Rhine Graben region

The collocation method was used to compute water vapor fields for the Upper Rhine Graben (URG) region from GNSS zenith total delays (ZTDs) and InSAR double difference slant delays (ddSTDs). Furthermore, mean temperature from ERA data was used for the conversion of GNSS ZTDs into IWV. The input data are hourly GNSS tropospheric parameters from the GURN (GNSS Upper Rhine Graben network) network for 4 different seasons in the period 2016-2018, as well as ddSTDs for 168 InSAR acquisition epochs of the Sentinel 1A+B satellites. In total, our dataset includes 2D fields of integrated water vapor (IWV) and zenith total delays (ZTDs) as well as 3D 'tomographic' products in form of refractivity fields. For 4 specific seasonal periods, also hourly water vapor density fields are provided by exploiting the relations between IWV and water vapor density in the collocation scheme. The tropospheric fields are provided for the horizontal WRF grid of data assimilation subset of this joint data collection, whereas the 3D fields are computed up to 8 km height for 16 equally distributed layers

FRUIT QUALITY PARAMETERS OF FIVE PEAR CULTIVARS IN WESTERN KOSOVO

This field experiment was designed to assess the quality of the pear fruits through some parameters in five different cultivars: ‘Willaim’, ‘Abate Fettel’, ‘Passe Crassane’, ‘Cure’ and ‘Santa Maria’. The experiment was conducted during 2013 on a pear orchard of 10 ha, in the first year of production. Quality parameters investigated in a trial in Western Kosovo were diameter, height, weight, firmness and the sugar content. The survey was carried out in four repetitions, where the results were statistically processed by ANOVA test. The results showed that, the larger diameter of fruit, was reached on avarage on Passe Crassane cultivar (81.86 mm), while Abate Fettel cv. showed the highest values of the height (147 mm) whereas, the highest weight performed Passe Crassane cv. (290 g), the highest values of the firmness of the fruit were found on William cv. (7.79 kg/0.5 cm2), finally the highest values of the sugar content of the fruit showed Abate Fettel cv. (16.38)

An adaptive GNSS-based reduced dynamic approach for real time autonomous navigation from the Earth to the Moon

In this research study we describe our last effort in further improving the achievable Global Navigation Satellite System (GNSS) based navigation performance in an Earth-Moon Transfer Orbit (MTO). The GNSS-based orbital filter we implemented previously with a dynamic approach, is modified by adopting a reduced-dynamics approach, which, with a certain accuracy, compensates for the dynamic model errors using a process noise model that weights observational and dynamical errors. Empirical accelerations are estimated as part of the state vector in a Kalman filter, adopting a deterministic forces model. Unlike the implementations reported in the existing literature, typically for orbit determination in Low Earth Orbit (LEO), here, in order to correctly weight observational and dynamical errors in the full trajectory from the Earth to the Moon, characterized by very variable signals and geometry conditions, an adaptive tuning of the filter is adopted. The observational and dynamical errors are predicted as function of different parameters, i.e., the estimated carrier-to-noise-ratio of the signals at the receiver position, the tracking loops setting, the kinematic state of the receiver and the combination of orbital forces modelled at different altitudes. The implemented orbital filter together with the developed receiver are designed in order to provide real-time autonomous on-board navigation on the way from the Earth to the Moon. Following a description of the simulation models and assumptions and of the existing orbit determination approaches, the paper focusses on the implementation of the proposed adaptive reduced-dynamic orbital filter and finally presents its performance in a MTO