Deterministic Optimal Transport-based Gaussian Mixture Particle Filtering for Verifiable Applications
Computation
2025-01-30 v1 Optimization and Control
Abstract
Mixture-model particle filters such as the ensemble Gaussian mixture filter require a resampling procedure in order to converge to exact Bayesian inference. Canonically, stochastic resampling is performed, which provides useful samples with no guarantee of usefulness for a finite ensemble. We propose a new resampling procedure based on optimal transport that deterministically selects optimal resampling points. We show on a toy 3-variable problem that it significantly reduces the amount of particles required for useful state estimation. Finally, we show that this filter improves the state estimation of a seldomly-observed space object in an NRHO around the moon.
Cite
@article{arxiv.2501.17302,
title = {Deterministic Optimal Transport-based Gaussian Mixture Particle Filtering for Verifiable Applications},
author = {Andrey A Popov and Renato Zanetti},
journal= {arXiv preprint arXiv:2501.17302},
year = {2025}
}