LEO-PNT Mega-Constellations: a New Design Driver for the Next Generation MEO GNSS Space Service Volume and Spaceborne Receivers

Menzione, Francesco; Paonni, Matteo

doi:10.1109/plans53410.2023.10140052

Cited by 8 publications

(19 citation statements)

References 36 publications

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“…This derives from a data-driven model reconstruction, providing antenna pattern expected mean and variance distributions. Such an approach is here implemented and adopted for the definition of the Galileo Reference Antenna Pattern (GRAP), for which metadata have recently been published in [8]. The proposed reconstructed model is an efficient and compact tool to provide the GNSS user and community with the most stable representation of the expected Galileo FOC transmitting antenna pattern, according to the knowledge matured at system level before the deployment of the service.…”

Section: Introductionmentioning

confidence: 99%

“…The first contribution is a detailed description of the GRAP model synthesis [8]. The procedure ingests the information retrieved during the antenna characterization campaign performed on the Galileo FOC satellite up to the highest available off-boresight angles, before their integration on the satellites.…”

Section: Introductionmentioning

confidence: 99%

“…The reconstructed patterns from different data collections are addressed as different realizations of the same model and merged, in order to achieve a general and easy-to-use representation of the antenna for the final GNSS space user. One of the most relevant features of the proposed statistical process is the possibility to define the GRAP values and their correspondent confidence bounds, so the idea is to complement the antenna pattern metadata [8] with information representative of the uncertainty level associated with the different pattern realizations. This level of confidence can be reflected in the user's analysis; hence, their conclusions inherit the capability to assess what will likely happen according to current knowledge of the pattern.…”

Section: Introductionmentioning

confidence: 99%

“…This level of confidence can be reflected in the user's analysis; hence, their conclusions inherit the capability to assess what will likely happen according to current knowledge of the pattern. For the sake of brevity and without loss of generality, all the results presented hereafter refer to the Galileo E1 carrier frequency, but extension to other Galileo frequencies can be easily derived by using the metadata provided in [8] and adjusting all frequency-dependent parameters.…”

Section: Introductionmentioning

confidence: 99%

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3D Galileo Reference Antenna Pattern for Space Service Volume Applications

Menzione,

Paonni

2024

Sensors

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There is an increasing demand for navigation capability for space vehicles. The exploitation of the so-called Space Service Volume (SSV), and hence the extension of the Global Navigation Satellite System (GNSS) from terrestrial to space users, is currently considered a fundamental step. Knowledge of the constellation antenna pattern, including the side lobe signals, is the main input for assessing the expected GNSS signal availability and navigation performance, especially for high orbits. The best way to define and share this information with the final GNSS user is still an open question. This paper proposes a novel methodology for the definition of a high-fidelity and easy-to-use statistical model to represent GNSS constellation antenna patterns. The reconstruction procedure, based on antenna characterization techniques and statistical learning, is presented here through its successful implementation for the “Galileo Reference Antenna Pattern (GRAP)” model, which has been proposed as the reference model for the Galileo programme. The GRAP represents the expected Equivalent Isotropic Radiated Power (EIRP) variation for the Galileo FOC satellites, and it is obtained by processing the measurements retrieved during the characterization campaign performed on the Galileo FOC antennas. The mathematical background of the model is analyzed in depth in order to better assess the GRAP with respect to different objectives such as improved resolution, smoothness and proper representation of the antenna pattern statistical distribution. The analysis confirms the enhanced GRAP properties and envisages the possibility of extending the approach to other GNSSs. The discussion is complemented by a preliminary use case characterization of the Galileo performance in SSV. The accessibility, a novel indicator, is defined in order to represent in a quick and compact manner, the expected Galileo SSV quality for different altitudes and target mission requirements. The SSV characterization is performed to demonstrate how simply and effectively the GRAP model can be inserted into user analysis. The work creates the basis for an improved capability for assessing Galileo-based navigation in SSV according to the current knowledge of the antenna pattern.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D Galileo Reference Antenna Pattern for Space Service Volume Applications

Menzione,

Paonni

2024

Sensors

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“…A complementary solution based on LEO could be, for example, to have GNSS receivers on board LEO satellites and to enable LEO to rebroadcast GNSS-like signals on other frequency bands [9]. This setup can improve the clock stability in LEO satellites by using GNSS clock as a reference.…”

Section: Introduction and Motivation 1leo-pnt Versus Gnssmentioning

confidence: 99%

Ionospheric Error Models for Satellite-Based Navigation—Paving the Road towards LEO-PNT Solutions

Imad,

Grenier,

Zhang

et al. 2023

Computers

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Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

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