Adaptive Gaussian Mixture Filtering for Multi-sensor Maneuvering Cislunar Space Object Tracking

Abstract

Successful space domain awareness (SDA) requires maintaining track custody of cooperative and noncooperative cislunar space objects (CSOs) through both ballistic and maneuvering trajectories. The surveillance of CSOs is particularly challenging due to the underlying chaotic multi-body dynamics, which makes uncertainty propagation more difficult when compared to Keplerian orbits. While methods exist for tracking cooperative spacecraft using high accuracy range measurements, the problem of passive noncooperative maneuvering CSO tracking has received considerably less attention. In this paper, CSO motion is modeled as a jump Markov system (JMS), where the CSO modality is unknown and subject to random switching. A novel adaptive Bayesian filter is proposed and shown to successfully maintain CSO track custody through both ballistic and maneuvering phases of an Artemis I-like trajectory.