The University of Southampton
University of Southampton Institutional Repository

Online estimation of ship dynamic flexure model parameters for transfer alignment

Online estimation of ship dynamic flexure model parameters for transfer alignment
Online estimation of ship dynamic flexure model parameters for transfer alignment
This paper presents an online approach for estimating the dynamic flexure model parameters in shipboard transfer alignment (TA). Traditionally, the application of Kalman filters (KFs) to the TA process is often restricted because of the lack of real-time information on dynamic flexure characteristics, and a KF designed on the basis of inaccurate parameters of the dynamic flexure model will result in a large alignment error. To overcome this difficulty, a parameter estimation algorithm is proposed in this paper, which utilizes the angular increment difference measured by the master inertial navigation system (MINS) and the slave inertial navigation system. Specifically, the Tufts–Kumaresan method is introduced to compute the unknown parameters of the dynamic flexure model from the angular increment correlation function. Our simulation results show that the proposed method can estimate the dynamic flexure parameters with a high degree of accuracy, even in low signal-to-noise ratio conditions. This parameter estimation method does not require a priori knowledge of dynamic flexure characteristics and, therefore, provides the shipboard sensors with an accurate and rapid-response capability for alignment with the MINS.
dynamic flexure, gausss-markov process, tufts-kumaresan method, parameter estimation, transfer alignment
1063-6536
1666-1678
Wu, Wei
f754f24b-bcdb-4f7b-836e-29cbc32aaba6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Qin, Shiqiao
24ce98b9-1368-43b8-8c03-047931a898fe
Wu, Wei
f754f24b-bcdb-4f7b-836e-29cbc32aaba6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Qin, Shiqiao
24ce98b9-1368-43b8-8c03-047931a898fe

Wu, Wei, Chen, Sheng and Qin, Shiqiao (2013) Online estimation of ship dynamic flexure model parameters for transfer alignment. IEEE Transactions on Control Systems Technology, 21 (5), 1666-1678. (doi:10.1109/TCST.2012.2214778).

Record type: Article

Abstract

This paper presents an online approach for estimating the dynamic flexure model parameters in shipboard transfer alignment (TA). Traditionally, the application of Kalman filters (KFs) to the TA process is often restricted because of the lack of real-time information on dynamic flexure characteristics, and a KF designed on the basis of inaccurate parameters of the dynamic flexure model will result in a large alignment error. To overcome this difficulty, a parameter estimation algorithm is proposed in this paper, which utilizes the angular increment difference measured by the master inertial navigation system (MINS) and the slave inertial navigation system. Specifically, the Tufts–Kumaresan method is introduced to compute the unknown parameters of the dynamic flexure model from the angular increment correlation function. Our simulation results show that the proposed method can estimate the dynamic flexure parameters with a high degree of accuracy, even in low signal-to-noise ratio conditions. This parameter estimation method does not require a priori knowledge of dynamic flexure characteristics and, therefore, provides the shipboard sensors with an accurate and rapid-response capability for alignment with the MINS.

Text
TCST2013-Sept.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: September 2013
Keywords: dynamic flexure, gausss-markov process, tufts-kumaresan method, parameter estimation, transfer alignment
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 356010
URI: http://eprints.soton.ac.uk/id/eprint/356010
ISSN: 1063-6536
PURE UUID: 7ff4798a-d54f-49ff-8da0-3b1945aeea71

Catalogue record

Date deposited: 10 Sep 2013 09:32
Last modified: 18 Nov 2019 20:37

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×