A study on the implementation of nonlinear Kalman filter applying MMG model

Koike, Hiroaki; Dostal, Leo; Sawada, Ryohei et al.

Journal of Marine Science and Technology (Japan), 2023, 28(4), 733-745

Number of Access:304（2025-07-16 16:28 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93290

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Title
A study on the implementation of nonlinear Kalman filter applying MMG model
Creator
CreatorName
Koike, Hiroaki
CreatorName
Dostal, Leo
NRID
1000000825911 1000000825911
CreatorName
Sawada, Ryohei
CreatorName
Miyauchi, Yoshiki
NRID
1000050556496 1000050556496
ORCID
0000-0002-2819-1297 0000-0002-2819-1297
researchmap permalink
atsuo_maki atsuo_maki
Scopus Author ID
24503751100 24503751100
CreatorName
Maki, Atsuo
Subject
EnKF
Kalman filter
MMG model
State estimation
UKF
Description
The version of record of this article, first published in Journal of Marine Science and Technology (Japan), is available online at Publisher’s website: https://doi.org/10.1007/s00773-023-00953-6
Abstract
Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.
Publisher
Springer
Source Title
Journal of Marine Science and Technology (Japan)
Volume (Issue)
28(4)
Page
733-745
Date of Issued
2023-10-28
Language
English
Handle URL
https://hdl.handle.net/11094/93290
PISSN
09484280
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1007/s00773-023-00953-6
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article