A Numerically Robust Bayesian Filtering Algorithm for Gaussian Mixture Models

被引：0

作者：

Wills, Adrian G. ^{[1
]}

Hendriks, Johannes ^{[2
]}

Renton, Christopher ^{[1
]}

Ninness, Brett ^{[1
]}

机构：

[1] Univ Newcastle, Sch Engn, Callaghan, NSW, Australia

[2] Australian Natl Univ, Coll Engn & Comp Sci, Canberra, ACT, Australia

来源：

IFAC PAPERSONLINE | 2023年 / 56卷 / 01期

关键词：

Bayesian filtering; gaussian mixture; state-space;

D O I：

10.1016/j.ifacol.2023.02.012

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 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 update steps. In addition, a numerically robust square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems, including the state estimation for a challenging non-linear system. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (<THESTERM>https://creativecommons.org/licenses/by-ne-nd/4.0/</THESTERM>)

引用

页码：67 / 72

页数：6

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