Continuous-time filtering in Lie groups: estimation via the Fr{é}chet mean of solutions to stochastic differential equations

Fuente: arXiv
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Main Authors: Bénéfice, Magalie, Arnaudon, Marc, Giremus, Audrey
Format: Preprint
Published: 2025
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author Bénéfice, Magalie
Arnaudon, Marc
Giremus, Audrey
author_facet Bénéfice, Magalie
Arnaudon, Marc
Giremus, Audrey
contents We compute the Fréchet mean $\mathscr{E}_t$ of the solution $X_{t}$ to a continuous-time stochastic differential equation in a Lie group. It provides an estimator with minimal variance of $X_{t}$. We use it in the context of Kalman filtering and more precisely to infer rotation matrices. In this paper, we focus on the prediction step between two consecutive observations. Compared to state-of-the-art approaches, our assumptions on the model are minimal.
format Preprint
id arxiv_https___arxiv_org_abs_2504_13502
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Continuous-time filtering in Lie groups: estimation via the Fr{é}chet mean of solutions to stochastic differential equations
Bénéfice, Magalie
Arnaudon, Marc
Giremus, Audrey
Probability
Signal Processing
Statistics Theory
We compute the Fréchet mean $\mathscr{E}_t$ of the solution $X_{t}$ to a continuous-time stochastic differential equation in a Lie group. It provides an estimator with minimal variance of $X_{t}$. We use it in the context of Kalman filtering and more precisely to infer rotation matrices. In this paper, we focus on the prediction step between two consecutive observations. Compared to state-of-the-art approaches, our assumptions on the model are minimal.
title Continuous-time filtering in Lie groups: estimation via the Fr{é}chet mean of solutions to stochastic differential equations
topic Probability
Signal Processing
Statistics Theory
url https://arxiv.org/abs/2504.13502