eCite Digital Repository

Position estimation for underwater vehicles using unscented Kalman filter with Gaussian process prediction

Citation

Ariza Ramirez, W and Leong, ZQ and Nguyen, H and Jayasinghe, SG, Position estimation for underwater vehicles using unscented Kalman filter with Gaussian process prediction, Underwater Technology, 36, (2) pp. 29-35. ISSN 1756-0543 (2019) [Refereed Article]


Preview
PDF (Published version)
765Kb
  

Copyright Statement

Copyright 2019. Society for Underwater Technology. This article is Open Access under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. (https://creativecommons.org/licenses/by/4.0/)

DOI: doi:10.3723/ut.36.029

Abstract

The present paper explores the use of Gaussian processunscented Kalman filter (GP-UKF) algorithm for position estimation of underwater vehicles. GP-UKF has a number of advantages over parametric unscented Kalman filters (UKFs) and Bayesian filters, such as improved tracking quality and graceful degradation with the increase of model uncertainty. The advantage of Gaussian process (GP) over parametric models is that GP considers noise and uncertainty in model identification. These qualities are highly desired for underwater vehicles as the number and quality of sensors available for position estimation are limited. The application of non-parametric models on navigation for underwater vehicles can lead to faster deployment of the platform, reduced costs and better performance than parametric methodologies. In the present study, a REMUS 100 parametric model was employed for the generation of data and internal model in the calculation to compare the performance of an ideal UKF against GP-UKF for position estimation. GP-UKF demonstrated better performance and robustness in the estimation of vehicle position and state correction compared to the ideal UKF.

Item Details

Item Type:Refereed Article
Keywords:machine learning, AUV, positioning, unscented Kalman filter, GP-UKF, Gaussian process, underwater vehicles
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Simulation, modelling, and programming of mechatronics systems
Objective Division:Transport
Objective Group:Water transport
Objective Field:Autonomous water vehicles
UTAS Author:Ariza Ramirez, W (Mr Wilmer Ariza Ramirez)
UTAS Author:Leong, ZQ (Dr Zhi Leong)
UTAS Author:Nguyen, H (Dr Hung Nguyen)
UTAS Author:Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage)
ID Code:145438
Year Published:2019
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2021-07-21
Last Modified:2021-09-15
Downloads:0

Repository Staff Only: item control page