Real-Time Thermospheric Density Estimation from Satellite Position Measurements
Name
J25_2020.pdf
Description
Accepted version
Size
4.25 MB
Format
Unknown
Checksum (MD5)
74ed87afc650d3eee6a165ca78d2546c
Author(s)
Mehta, Piyush M
Linares, Richard
Date Issued
2020
Journal
Journal of Guidance Control and Dynamics
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Version
Author's final manuscript
Abstract
In this paper, a new data-driven method is demonstrated for real-time neutral density estimation via model–data fusion in quasi-physical ionosphere–thermosphere models. The proposed method has two main components: 1) the use of a quasi-physical reduced-order model (ROM) to represent the dynamics of the upper atmosphere, and 2) the calibration of the ROM coefficients using satellite position measurements. The ROM is developed using dynamic mode decomposition with control. Previous work required direct density measurements (accelerometer-derived densities), and the current work extends this approach to satellite position measurements. This work is a new approach to dynamic calibration of the atmosphere. This work proposes combining the orbit determination process with the ROM coefficient calibration through the use of the square-root unscented Kalman filter (SQUKF). The proposed SQUKF allows for new potential data sources to be incorporated into the density calibration process. This is demonstrated with simulated Global Positioning System position measurements with 5 min resolution and 10 m Cartesian position error. The proposed method is demonstrated to be simple, robust, and accurate through simulation scenarios. The proposed method can provide real-time estimates of the state of the upper atmosphere while having inherent forecasting/predictive capabilities.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/1.G004793
