EMBRACE:SEED - Orbital Drag and Neutral Atmosphere Density from Assimilation of Satellite Global Positioning System (GPS) Navigation Data

Even at low-Earth orbit altitudes there is enough atmosphere that its drag effect on satellites must be considered for accurate navigation in this increasingly crowded region of space. During space weather events, heating of the atmosphere can cause it to expand to higher altitude, causing dramatic and sudden increases in atmospheric drag. There are examples of satellites which have re-entered the atmosphere prematurely because of this increased drag.

There are relatively few direct measurements of the atmospheric density at these altitudes to use to improve models, but the satellite navigation data (e.g. from GPS), can be used to detect the minute changes in orbits caused by this drag, which can be inverted to determine atmospheric density. These observations can in turn be used to improve atmospheric models.

In this project we will develop the necessary tools and models to obtain atmospheric density from navigation data, and apply them to actual satellite orbit data. In the future such tools can be applied on a large scale to the navigation data of thousands of orbiting satellites to develop more accurate atmospheric models which will help all satellite operators to keep satellites operating safely.

The proposal aims to develop data assimilation algorithms that will enable estimation of the atmospheric drag on satellites in low-Earth orbit (LEO) based on their low-resolution Global Navigation Satellite System (GNSS) navigation data. Drag is defined as a loss of total energy of the satellite due to the atmosphere only. After the effect of solar radiation pressure (which is vectorized and can both decrease and increase total energy), it is possible to determine the atmospheric mass density with appropriately calibrated drag coefficients.

The data assimilation algorithms to determine drag consist of a Kalman filter approach wrapped around an orbit propagator which incorporates a high-resolution gravitational field model, taking as input the low-resolution GNSS navigation data of the satellites. This study will identify the methods for separating radiation pressure and atmospheric drag depending on orbital location and satellite orientation.

In the future, these algorithms and methodologies can be used to produce a large database of atmospheric densities which can be incorporated into whole atmosphere data assimilation models for nowcasting and forecasting atmospheric density. The methods and algorithms can also be developed further to form the basis for on-board algorithms for satellites to autonomously take evasive action.

Existing tools for atmospheric drag measurements are largely proprietary. The tools developed through this effort will be made publicly available, expanding access to cutting-edge research capabilities and supporting scientific innovation across the academic community. This project is jointly funded by Aeronomy and EMBRACE programs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.