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arXiv · 2501.17302

Deterministic Optimal Transport-based Gaussian Mixture Particle Filtering for Verifiable Applications

Andrey A Popov·Renato Zanetti
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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.

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Andrey A Popov, Renato Zanetti. 2025-01-28. Deterministic Optimal Transport-based Gaussian Mixture Particle Filtering for Verifiable Applications. https://arxiv.org/abs/2501.17302

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