A deep neural network framework with Analytic Continuation for predicting hypervelocity fragment flyout from satellite explosions

Abstract

Hypervelocity breakup events contribute to the rapidly growing population of space debris orbiting Earth. To ensure a safe environment for future space missions, it is crucial to accurately characterize resulting fragments, which constitute a major portion of the orbital population but are often too small for conventional tracking methods. Currently, the Space Surveillance Network tracks satellites and debris within a specific altitude range and releases the data publicly as Two-Line Elements, though these datasets are limited in both size and accuracy. To supplement Two-Line Element data, this paper presents a novel Deep Neural Network approach to estimate orbital elements of hypervelocity fragments resulting from simulated satellite explosions. It employs initial conditions collected from realistic terrestrial explosions considered to be relative to 5 different detonation points on the polar orbit of the weather satellite, NOAA-16, to model the initial states of debris fragments immediately following an explosion. The debris trajectories are then propagated using a high-precision semi-analytic integration method, Analytic Continuation, considering $J_2-J_6$ zonal gravitational terms and Earth’s atmospheric drag perturbations. The collected data is used to train a Deep Neural Network for each of the orbital elements. Finally, a testing data set is used to validate the results, finding that this technique accurately estimates most of the orbital elements, but is not suited to predict the true anomaly of the fragments. Therefore, K-Nearest Neighbor regression is utilized instead to predict the nature of the final orbital element.