3D Galileo Reference Antenna Pattern for Space Service Volume Applications
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...
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| Format: | Article |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2220 |
| _version_ | 1827286530271477760 |
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| author | Francesco Menzione Matteo Paonni |
| author_facet | Francesco Menzione Matteo Paonni |
| author_sort | Francesco Menzione |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:35:01Z |
| format | Article |
| id | doaj.art-d4e2e1d7f25e48cea417c22fbf89e660 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:35:01Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d4e2e1d7f25e48cea417c22fbf89e6602024-04-12T13:26:30ZengMDPI AGSensors1424-82202024-03-01247222010.3390/s240722203D Galileo Reference Antenna Pattern for Space Service Volume ApplicationsFrancesco Menzione0Matteo Paonni1European Commission, Joint Research Centre (JRC), 21027 Ispra, ItalyEuropean Commission, Joint Research Centre (JRC), 21027 Ispra, ItalyThere 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.https://www.mdpi.com/1424-8220/24/7/2220GNSSspace service volumeGalileo antenna patternantenna characterizationspherical harmonicstatistical learning |
| spellingShingle | Francesco Menzione Matteo Paonni 3D Galileo Reference Antenna Pattern for Space Service Volume Applications Sensors GNSS space service volume Galileo antenna pattern antenna characterization spherical harmonic statistical learning |
| title | 3D Galileo Reference Antenna Pattern for Space Service Volume Applications |
| title_full | 3D Galileo Reference Antenna Pattern for Space Service Volume Applications |
| title_fullStr | 3D Galileo Reference Antenna Pattern for Space Service Volume Applications |
| title_full_unstemmed | 3D Galileo Reference Antenna Pattern for Space Service Volume Applications |
| title_short | 3D Galileo Reference Antenna Pattern for Space Service Volume Applications |
| title_sort | 3d galileo reference antenna pattern for space service volume applications |
| topic | GNSS space service volume Galileo antenna pattern antenna characterization spherical harmonic statistical learning |
| url | https://www.mdpi.com/1424-8220/24/7/2220 |
| work_keys_str_mv | AT francescomenzione 3dgalileoreferenceantennapatternforspaceservicevolumeapplications AT matteopaonni 3dgalileoreferenceantennapatternforspaceservicevolumeapplications |