Probabilistic Methods for Initial Orbit Determination and Orbit Determination in Cislunar Space

2026-02-20

AI Summary

This work addresses the limitations of traditional Gaussian initial orbit determination methods, which rely on the two-body assumption and fail in the Earth–Moon three-body dynamical environment. To overcome this, the authors propose a probabilistic orbit determination framework that minimizes modeling assumptions: it first generates an initial state estimate in the form of a particle cloud by kinematically fitting consecutive ground-based observations, then recursively refines this estimate using a Particle Gaussian Mixture (PGM) filter to reduce uncertainty. This approach uniquely integrates assumption-lean initial orbit generation with PGM filtering, making it well-suited for complex Earth–Moon space environments. Experimental results demonstrate that the proposed framework consistently outperforms conventional filtering methods across diverse Earth–Moon orbit types, significantly enhancing both robustness and accuracy in orbit determination.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
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Application Category

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Abstract

In orbital mechanics, Gauss's method for orbit determination (OD) is a popular, minimal assumption solution for obtaining the initial state estimate of a passing resident space object (RSO). Since much of the cislunar domain relies on three-body dynamics, a key assumption of Gauss's method is rendered incompatible, creating a need for a new, minimal assumption method for initial orbit determination (IOD). In this work, we present a framework for short and long term probabilistic target tracking in cislunar space which produces an initial state estimate with as few assumptions as possible. Specifically, we propose an IOD method involving the kinematic fitting of several series of noisy, consecutive ground-based observations. Once a probabilistic initial state estimate in the form of a particle cloud is formed, we apply the powerful Particle Gaussian Mixture (PGM) Filter to reduce the uncertainty of our state estimate over time. This combined IOD/OD framework is demonstrated for several classes of trajectories in cislunar space and compared to better-known filtering frameworks.

Problem

• Initial Orbit Determination
• Cislunar Space
• Orbit Determination
• Three-body Dynamics
• Resident Space Object

Innovation

• Initial Orbit Determination
• Cislunar Space
• Probabilistic Tracking
• Particle Gaussian Mixture Filter
• Three-body Dynamics