On the Constellation Design of Multi-GNSS Reflectometry Mission Using the Particle Swarm Optimization Algorithm

Yi Han,Xiaohua Xu,Jia Luo

doi:10.3390/atmos10120807

Abstract

Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.

Highlights

Over the past few decades, global navigation satellite systems (GNSSs) have been successfully applied for positioning, navigation, and timing as well as for Earth remote sensing
The application of GNSS reflectometry data for the ocean altimetry was first proposed by Martin-Neira [3], and the global positioning system (GPS) reflected signals were used to sense the roughness of the reflecting surface, which was demonstrated by Garrison et al [4]
CYclone Global Navigation Satellite System (CYGNSS) is composed of eight low Earth orbit (LEO) satellites [10], and a 24-satellite polar orbiting constellation was taken into consideration in the simulation work of Zavorotny et al [2]

Summary

Introduction

Over the past few decades, global navigation satellite systems (GNSSs) have been successfully applied for positioning, navigation, and timing as well as for Earth remote sensing. GNSS-Reflectometry (GNSS-R) is an innovative remote sensing technique that uses the GNSS signals reflected from the Earth’s surface to derive a variety of geophysical parameters [1,2]. The application of GNSS reflectometry data for the ocean altimetry was first proposed by Martin-Neira [3], and the global positioning system (GPS) reflected signals were used to sense the roughness of the reflecting surface, which was demonstrated by Garrison et al [4]. Atmosphere 2019, 10, 807 an Earth-reflected GPS signal, which was obtained by the space shuttle spaceborne imaging radar-C (SIR-C) at an altitude of 200 km, was an initial measurement to scale GNSS-R measurements to obtain the expected signal-to-noise ratio (SNR) for estimating geophysical parameters [5]. The remote-sensing measurement using GPS reflection signals was later performed by the UK-Disaster

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Discussion

Conclusion