A Bayesian Filtering Algorithm for Gaussian Mixture Models
Machine Learning
2023-07-03 v2 Systems and Control
Abstract
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.
Cite
@article{arxiv.1705.05495,
title = {A Bayesian Filtering Algorithm for Gaussian Mixture Models},
author = {Adrian G. Wills and Johannes Hendriks and Christopher Renton and Brett Ninness},
journal= {arXiv preprint arXiv:1705.05495},
year = {2023}
}