Tracking an Untracked Space Debris After an Inelastic Collision Using Physics Informed Neural Network

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 collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN )based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better prediction for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.