Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning

TL;DR

This paper applies maximum causal entropy inverse reinforcement learning to analyze and predict space object maneuvering behavior from observational data, focusing on LEO and GEO station-keeping scenarios.

Contribution

It introduces a novel inverse RL approach for space objects that estimates their reward functions from observed trajectories under disturbances.

Findings

Successfully models SO maneuvering behavior.

Applicable to LEO and GEO station-keeping.

Provides a framework for analyzing space object control strategies.

Abstract

Inverse Reinforcement Learning (RL) can be used to determine the behavior of Space Objects (SOs) by estimating the reward function that an SO is using for control. The approach discussed in this work can be used to analyze maneuvering of SOs from observational data. The inverse RL problem is solved using maximum causal entropy. This approach determines the optimal reward function that a SO is using while maneuvering with random disturbances by assuming that the observed trajectories are optimal with respect to the SO's own reward function. Lastly, this paper develops results for scenarios involving Low Earth Orbit (LEO) station-keeping and Geostationary Orbit (GEO) station-keeping.