Tracking an Untracked Space Debris After an Inelastic Collision Using Physics Informed Neural Network
Harsha M., Gurpreet Singh, Vinod Kumar, Arun Balaji Buduru, Sanat K., Biswas

TL;DR
This paper introduces a Physics Informed Neural Network approach to estimate the trajectory and properties of untracked space debris after inelastic collisions, improving prediction accuracy over classical methods.
Contribution
The paper develops a PINN-based method for tracking space debris post-collision, demonstrating superior performance over traditional optimization techniques.
Findings
PINN outperforms classical optimization in debris trajectory prediction
Simulated 8565 inelastic collision events for validation
PINN achieves lower RMSE and better estimates of debris properties
Abstract
With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris' trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN) - based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic…
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Taxonomy
TopicsSpace Satellite Systems and Control · Anomaly Detection Techniques and Applications
