Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation
Abstract As the number of man‐made Earth‐orbiting objects increases, satellite operators need enhanced space traffic management capabilities to ensure safe space operations. For objects in Low Earth orbit, orbit determination and prediction require accurate estimates of the local thermospheric densi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002620 |
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| author | David J. Gondelach Richard Linares |
| author_facet | David J. Gondelach Richard Linares |
| author_sort | David J. Gondelach |
| collection | DOAJ |
| description | Abstract As the number of man‐made Earth‐orbiting objects increases, satellite operators need enhanced space traffic management capabilities to ensure safe space operations. For objects in Low Earth orbit, orbit determination and prediction require accurate estimates of the local thermospheric density. In previous work, the estimation of thermospheric densities using two‐line element data and a reduced‐order model for the upper atmosphere was demonstrated. In this study we demonstrate an approach for density estimation using radar and GPS tracking data. For this, we assimilate the tracking data in a dynamic reduced‐order density model using a Kalman filter by simultaneously estimating the orbits and global density. We used the radar range and range rate measurements of 20 objects and the GPS position measurements of 10 commercial satellites. The estimated density was validated against accurate SWARM density data and compared with NRLMSISE‐00, JB2008, and two‐line element (TLE)‐estimated densities. We found that the estimated densities are significantly more accurate than NRLMSISE‐00 and JB2008 densities. In particular, using the GPS data of 10 satellites, we obtain density estimates with a daily 1‐σ error of only 5% compared to 14% and 22% for empirical models and 10% for TLE‐estimated density. These accurate density estimates can be used to improve orbit determination and the use of real‐time tracking data would enable real‐time density estimation. |
| format | Article |
| id | doaj-art-914c57dd0f21484d8944fe2b530161a4 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-914c57dd0f21484d8944fe2b530161a42025-01-14T16:31:29ZengWileySpace Weather1542-73902021-04-01194n/an/a10.1029/2020SW002620Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data AssimilationDavid J. Gondelach0Richard Linares1Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USADepartment of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USAAbstract As the number of man‐made Earth‐orbiting objects increases, satellite operators need enhanced space traffic management capabilities to ensure safe space operations. For objects in Low Earth orbit, orbit determination and prediction require accurate estimates of the local thermospheric density. In previous work, the estimation of thermospheric densities using two‐line element data and a reduced‐order model for the upper atmosphere was demonstrated. In this study we demonstrate an approach for density estimation using radar and GPS tracking data. For this, we assimilate the tracking data in a dynamic reduced‐order density model using a Kalman filter by simultaneously estimating the orbits and global density. We used the radar range and range rate measurements of 20 objects and the GPS position measurements of 10 commercial satellites. The estimated density was validated against accurate SWARM density data and compared with NRLMSISE‐00, JB2008, and two‐line element (TLE)‐estimated densities. We found that the estimated densities are significantly more accurate than NRLMSISE‐00 and JB2008 densities. In particular, using the GPS data of 10 satellites, we obtain density estimates with a daily 1‐σ error of only 5% compared to 14% and 22% for empirical models and 10% for TLE‐estimated density. These accurate density estimates can be used to improve orbit determination and the use of real‐time tracking data would enable real‐time density estimation.https://doi.org/10.1029/2020SW002620GPS dataradar tracking datareduced‐order modelingsatellite dragthermospheric density estimationthermospheric density modeling |
| spellingShingle | David J. Gondelach Richard Linares Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation Space Weather GPS data radar tracking data reduced‐order modeling satellite drag thermospheric density estimation thermospheric density modeling |
| title | Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation |
| title_full | Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation |
| title_fullStr | Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation |
| title_full_unstemmed | Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation |
| title_short | Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation |
| title_sort | real time thermospheric density estimation via radar and gps tracking data assimilation |
| topic | GPS data radar tracking data reduced‐order modeling satellite drag thermospheric density estimation thermospheric density modeling |
| url | https://doi.org/10.1029/2020SW002620 |
| work_keys_str_mv | AT davidjgondelach realtimethermosphericdensityestimationviaradarandgpstrackingdataassimilation AT richardlinares realtimethermosphericdensityestimationviaradarandgpstrackingdataassimilation |