Bayesian Inference and Prediction of Wave-induced Ship Motion based on Discrete-frequency Model Approximations⁎. Issue 29 (2018)

• Record Type: Journal Article
• Authors: Kennedy, Justin M.; Ford, Jason J.; Valentinis, Francis; Perez, Tristan
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 29(2018)
• Page Start: 104
• Page End: 109
• Publication Date: 2018
• Subjects: Statistical Inference -- Detection Algorithms -- Bayesian Inference; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.477
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, we investigate the use of a discrete-frequency approximation for stochastic processes, modelling wave-induced ship motion and assess its prediction performance. The proposed estimator is obtained by adapting Bayesian spectral inference methods. We study the relationship between the lag window and the prediction performance and suggest a minimum requirement on window length for minimising the prediction error. We show an application to prediction of the pitch, heave, and roll motion from experiments of a scale-model container ship.