Rachit Bhatia, West Virginia University; Piyush Mehta, West Virginia University
Keywords: Half-life modeling, space weather, astrodynamics, dynamic LEO, space domain awareness
Abstract:
The precise characterization of atmospheric density is essential for accurate orbit propagation and reliable tracking of Resident Space Objects (RSOs) in Low Earth Orbit (LEO). The inherently dynamic character of atmospheric density introduces large uncertainties in predicting spacecraft trajectories. Enhanced space operations – detection, characterization, and tracking – are critical for safety and sustainability in proliferated LEO environment. Under support from the Intelligence Advanced Research Projects Activity (IARPA) Space Debris Identification and Tracking (SINTRA) program and the Office of Space Commerce (OSC), we are developing the next-generation drag modeling framework that accurately characterizes atmospheric density uncertainty due to space weather in a physics- and data-driven approach. Our last year’s AMOS research highlighted development of the Stochastic Unscented Transform (SUT), a mathematical formulation designed to capture the joint statistics of probabilistic atmospheric density models and their probabilistic drivers or inputs. This work builds upon this framework because numerical instabilities, such that a non-physical trend for the change in position standard deviation over time, were observed during long-term (4 days or more) propagation. The cause for these instabilities was found to be covariance orthogonalization technique used to model half-life trends for atmospheric density. Effectively modeling half-life, i.e., the temporal correlation, of atmospheric density is critical to realistically simulate a dynamic LEO environment. This study mitigates these instabilities by developing a novel approach to model half-life by using First-order Gauss-Markov (FOGM) processes as the dynamics for the atmospheric density variations. The methodology proposed here incorporates the uncertainties in atmospheric density while enabling accurate half-life modeling. With uncertainty incorporation and improved dynamics, the methodology accurately captures an environmental variation and its effect on spacecraft motion without introducing any extra architectural components.
The proposed method maps atmospheric density variability directly into the uncertainty in position and velocity, which is thereby propagated over time to enable stochastic modeling of LEO orbits. One of the primary advantages of the method is that it does not modify existing architecture for standard orbit propagation algorithms rather than enhance existing dynamics, making it straightforward to implement atmospheric density variability within current space operations. The state vector in this case contains spacecraft position, velocity, and the change in atmospheric density, respectively. The dynamics of the atmospheric density variations are modeled via a First-order Gauss-Markov process, which enables the precise modeling of various half-life values. Over the long-time context, this approach guarantees that the impacts of changing atmospheric density are properly represented in the positional and velocity characteristics of the spacecraft. The influence of changing half-lives on the growth of trajectories is validated using Monte Carlo simulations, which show that longer half-lives give larger orbital deviations.
The ability to parameterize the half-life parameter provides the analyst with a means to realistically model the evolving uncertainty in time, thus better capturing the time-correlated thermospheric dynamics than deterministic models. This characteristic allows for more accurate state estimation and predictive performance, both of which are necessary for the regulation of space traffic and assessing the risk of collision. The main findings from the simulations are that varying half-life values produce statistically realistic spacecraft position and velocity estimates. Through a controlled variation of the density half-life in the propagation model, this method allows for a more nuanced interpretation of atmospheric variability and orbital evolution.
The operational advantages achieved with this method are improved accuracy in orbit determination, less dependence on empirical drag models, and better resilience to density variations induced by solar maximum conditions. These enhancements have an essential role in enhancing conjunction assessment capabilities, satellite tracking reliability, and space traffic coordination by means of more effective arrangements. In addition, because the method applies established propagation methods without invoking extra computation resources, the technique is appropriate for application in realistic space operations.
Future directions for research involve validating proposed technique against real-time atmospheric density observations. Additionally, validation with observational data from ground- and space-based tracking sensors will also increase the credibility and usefulness of this modeling technique. The ability to account for variable atmospheric density half-lives in a physically consistent manner provides a valuable advantage for RSO tracking, particularly under unfavorable space weather conditions, thereby reducing false positive alerts and making LEO estimates more reliable.
Date of Conference: September 16-19, 2025
Track: Atmospherics/Space Weather