Semi-analytical model for the calculation of solar radiation pressure and its effects on a LEO satellite with predicting the change in position vectors using machine learning techniques

TL;DR

This paper presents a semi-analytical model for calculating solar radiation pressure effects on LEO satellites, combining physics-based analysis with machine learning to improve trajectory prediction accuracy.

Contribution

It introduces a novel hybrid approach that integrates analytical SRP modeling with machine learning techniques for adaptive satellite position prediction.

Findings

The model accurately simulates SRP effects on LEO satellite trajectories.

Machine learning enhances prediction accuracy through adaptive learning.

Comparative analysis shows improved ground track revisit times.

Abstract

The rapid increase in the deployment of Low Earth Orbit (LEO) satellites, catering to diverse applications such as communication, Earth observation, environmental monitoring, and scientific research, has significantly amplified the complexity of trajectory management. The current work focuses on calculating and analyzing perturbation effects on a satellite's anticipated trajectory in LEO, considering Solar Radiation Pressure (SRP) as the main perturbing force. The acceleration due to SRP and it's effects on the satellite was calculated using a custom-built Python module mainly based on the hypothesis of the cannonball model. The study demonstrates the effectiveness of the proposed model through comprehensive simulations and comparisons with existing analytical and numerical methods. Here, the primary Keplerian orbital characteristics were employed to analyze a simulated low-earth orbit LEO satellite, initially visualizing the satellite's trajectory and ground tracks at a designated altitude. The study also focuses on a comparative analysis of ground stations, primarily considering the main regions of the subcontinent, with revisit time as the key parameter for comparison. In the end, we combine analytical techniques with Machine Learning (ML) algorithms to predict changes in the position vectors of the satellite. Using ML techniques, the model can adaptively learn and refine predictions based on historical data and real-time input, thus improving accuracy over time. In addition, the incorporation of analytical methods allows for a deeper understanding of the underlying physics governing satellite motion, enabling more precise adjustments and corrections.