3D Galileo Reference Antenna Pattern for Space Service Volume Applications

Area of Science:

• Space engineering
• Satellite navigation systems
• Antenna theory

Background:

• Space vehicles require advanced navigation capabilities, driving the need to extend Global Navigation Satellite System (GNSS) to the Space Service Volume (SSV).
• Accurate constellation antenna patterns, including sidelobes, are crucial for assessing GNSS signal availability and navigation performance in high orbits.
• Current methods for defining and sharing antenna pattern information with space users are insufficient.

Purpose of the Study:

• To propose a novel methodology for a high-fidelity, user-friendly statistical model of GNSS constellation antenna patterns.
• To implement and validate this methodology using the Galileo Reference Antenna Pattern (GRAP) model for Galileo FOC satellites.
• To analyze the mathematical properties of the GRAP model and demonstrate its utility in assessing Galileo's performance in the SSV.

Main Methods:

• Development of a statistical model based on antenna characterization techniques and statistical learning.
• Processing of measurement data from Galileo FOC antenna characterization campaigns.
• Mathematical analysis of the GRAP model for resolution, smoothness, and statistical distribution accuracy.
• Definition and application of a novel 'accessibility' indicator for SSV quality assessment.

Main Results:

• Successful implementation of the GRAP model, representing Equivalent Isotropic Radiated Power (EIRP) variations for Galileo FOC satellites.
• Demonstration of enhanced GRAP properties, including improved resolution, smoothness, and statistical representation.
• Validation of the GRAP model's effectiveness in a preliminary Galileo SSV performance use case.
• Introduction of the 'accessibility' indicator for compact SSV quality representation.

Conclusions:

• The proposed methodology provides a high-fidelity, easy-to-use statistical model for GNSS antenna patterns.
• The GRAP model offers enhanced properties and demonstrates the potential for application to other GNSS constellations.
• The GRAP model effectively supports the assessment of Galileo-based navigation in the SSV, improving future space navigation capabilities.