Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation
Abstract Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real‐time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced‐order model for the thermospheric density that enables rea...
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Wiley
2020-02-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2019SW002356 |
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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 Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real‐time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced‐order model for the thermospheric density that enables real‐time density estimation using two‐line element (TLE) data. For this, the global thermospheric density is represented by the main spatial modes of the atmosphere and a time‐varying low‐dimensional state and a linear model is derived for the dynamics. Three different models are developed based on density data from the TIE‐GCM, NRLMSISE‐00, and JB2008 thermosphere models and are valid from 100 to maximum 800 km altitude. Using the models and TLE data, the global density is estimated by simultaneously estimating the density and the orbits and ballistic coefficients of several objects using a Kalman filter. The sequential estimation provides both estimates of the density and corresponding uncertainty. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer‐derived densities. The estimated densities are shown to be significantly more accurate and less biased than NRLMSISE‐00 and JB2008 modeled densities. The uncertainty in the density estimates is quantified and shown to be dependent on the geographical location, solar activity, and objects used for estimation. In addition, the data assimilation capability of the model is highlighted by assimilating CHAMP accelerometer‐derived density data together with TLE data to obtain more accurate global density estimates. Finally, the dynamic thermosphere model is used to forecast the density. |
| format | Article |
| id | doaj-art-bc7930aaa68a404291266c3bfebc18ae |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-bc7930aaa68a404291266c3bfebc18ae2025-01-14T16:30:20ZengWileySpace Weather1542-73902020-02-01182n/an/a10.1029/2019SW002356Real‐Time Thermospheric Density Estimation via Two‐Line Element 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 Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real‐time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced‐order model for the thermospheric density that enables real‐time density estimation using two‐line element (TLE) data. For this, the global thermospheric density is represented by the main spatial modes of the atmosphere and a time‐varying low‐dimensional state and a linear model is derived for the dynamics. Three different models are developed based on density data from the TIE‐GCM, NRLMSISE‐00, and JB2008 thermosphere models and are valid from 100 to maximum 800 km altitude. Using the models and TLE data, the global density is estimated by simultaneously estimating the density and the orbits and ballistic coefficients of several objects using a Kalman filter. The sequential estimation provides both estimates of the density and corresponding uncertainty. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer‐derived densities. The estimated densities are shown to be significantly more accurate and less biased than NRLMSISE‐00 and JB2008 modeled densities. The uncertainty in the density estimates is quantified and shown to be dependent on the geographical location, solar activity, and objects used for estimation. In addition, the data assimilation capability of the model is highlighted by assimilating CHAMP accelerometer‐derived density data together with TLE data to obtain more accurate global density estimates. Finally, the dynamic thermosphere model is used to forecast the density.https://doi.org/10.1029/2019SW002356density estimationthermospheric density modelingsatellite dragtwo‐line element datareduced‐order modeling |
| spellingShingle | David J. Gondelach Richard Linares Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation Space Weather density estimation thermospheric density modeling satellite drag two‐line element data reduced‐order modeling |
| title | Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation |
| title_full | Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation |
| title_fullStr | Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation |
| title_full_unstemmed | Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation |
| title_short | Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation |
| title_sort | real time thermospheric density estimation via two line element data assimilation |
| topic | density estimation thermospheric density modeling satellite drag two‐line element data reduced‐order modeling |
| url | https://doi.org/10.1029/2019SW002356 |
| work_keys_str_mv | AT davidjgondelach realtimethermosphericdensityestimationviatwolineelementdataassimilation AT richardlinares realtimethermosphericdensityestimationviatwolineelementdataassimilation |