Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-bas...

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Bibliographic Details
Published in:	Digital signal processing Vol. 148; p. 104445
Main Authors:	Liu, Yang, An, Shun, Wang, Longjin, He, Yan, Fan, Zhimin
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.05.2024
Subjects:	Data filtering technique Multi-innovation identification theory Parameter model identification Ship-wave discrete-time model Stochastic gradient algorithm Parameter model identification Ship-wave discrete-time model Stochastic gradient algorithm Data filtering technique Multi-innovation identification theory
ISSN:	1051-2004, 1095-4333
Online Access:	Get full text
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Summary:	This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification. •Based on the Eulerian discretization idea, a ship-wave discrete-time autoregressive moving average model with exogenous inputs is derived.•Introducing the filtering technique and the auxiliary model identification idea, a filtered stochastic gradient algorithm is proposed.•A filtering-based multi-innovation stochastic gradient algorithm is proposed based on filtered stochastic gradient identification.
ISSN:	1051-2004 1095-4333
DOI:	10.1016/j.dsp.2024.104445