Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
Using large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/2/333 |
| _version_ | 1827622303781879808 |
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| author | Jack Winkelried Christopher Ruf Scott Gleason |
| author_facet | Jack Winkelried Christopher Ruf Scott Gleason |
| author_sort | Jack Winkelried |
| collection | DOAJ |
| description | Using large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to those metrics. Orbits are defined by a set of Keplerian elements, and the relationship between those elements and the spatial and temporal coverage metrics are examined in order to maximize global and zonal (latitude-dependent) coverage. Additional optimization is performed by dividing a constellation into multiple orbit planes. An iterative method can be applied to this design process to compare the performance of current and previous designs. The main objective of this work is the design of optimized configurations of satellites in low Earth orbiting constellations to maximize the spatial and temporal sampling and coverage provided by its sensors. The key innovations developed are a new cost function which measures the temporal sampling properties of a satellite constellation, and the use of it together with existing cost functions for spatial sampling to design satellite constellations that optimize performance with respect to both performance metrics. |
| first_indexed | 2024-03-09T11:20:37Z |
| format | Article |
| id | doaj.art-ce3822f221e84e94a0cbf9361758570c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:20:37Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ce3822f221e84e94a0cbf9361758570c2023-12-01T00:19:02ZengMDPI AGRemote Sensing2072-42922023-01-0115233310.3390/rs15020333Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite ConstellationJack Winkelried0Christopher Ruf1Scott Gleason2Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USADaaxa LLC, Boulder, CO 80305, USAUsing large constellations of smallsats, mission designers can improve sampling density and coverage. We develop performance metrics that characterize key sampling properties for applications in numerical weather prediction and optimize orbit design parameters of the constellation with respect to those metrics. Orbits are defined by a set of Keplerian elements, and the relationship between those elements and the spatial and temporal coverage metrics are examined in order to maximize global and zonal (latitude-dependent) coverage. Additional optimization is performed by dividing a constellation into multiple orbit planes. An iterative method can be applied to this design process to compare the performance of current and previous designs. The main objective of this work is the design of optimized configurations of satellites in low Earth orbiting constellations to maximize the spatial and temporal sampling and coverage provided by its sensors. The key innovations developed are a new cost function which measures the temporal sampling properties of a satellite constellation, and the use of it together with existing cost functions for spatial sampling to design satellite constellations that optimize performance with respect to both performance metrics.https://www.mdpi.com/2072-4292/15/2/333constellation designCYGNSSGNSS reflectometrySpOCK |
| spellingShingle | Jack Winkelried Christopher Ruf Scott Gleason Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation Remote Sensing constellation design CYGNSS GNSS reflectometry SpOCK |
| title | Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation |
| title_full | Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation |
| title_fullStr | Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation |
| title_full_unstemmed | Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation |
| title_short | Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation |
| title_sort | spatial and temporal sampling properties of a large gnss r satellite constellation |
| topic | constellation design CYGNSS GNSS reflectometry SpOCK |
| url | https://www.mdpi.com/2072-4292/15/2/333 |
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