A Hybrid Particle Gaussian Mixture Filtering Method for Cislunar Orbit Determination Under Extreme Uncertainty

TL;DR

This paper presents a novel recursive probabilistic filtering method combining particle and Gaussian mixture techniques for orbit determination in the cislunar domain, where traditional methods fail due to three-body effects.

Contribution

The paper introduces a hybrid Particle Gaussian Mixture filtering approach specifically designed for cislunar orbit determination under extreme uncertainty, extending beyond classical two-body assumptions.

Findings

Successfully applied to cislunar orbit regime

Enables fusion of probabilistic data with angles-only observations

Effective for short and long-term target tracking

Abstract

Gauss's method of orbit determination (OD) and its variants are among the most popular initial state estimation techniques for astronomers and engineers alike. However, owing to its assumptions regarding the two-body problem, Gauss's method is inapplicable in the cislunar domain, where three body effects dominate. We introduce a hybrid Particle Gaussian Mixture filtering method, a purely recursive probabilistic orbit determination framework based on a combination of the Markov Chain Monte Carlo based Particle Gaussian Mixture-II (PGM-II) and Particle Gaussian Mixture-I (PGM-I) filters. This method enables us to fuse probabilistic information with angles-only observations from terrestrial telescopes for short and long-term cislunar target tracking. We demonstrate this technique on an important cislunar orbit regime.