Autonomous Underwater Vehicles Identification through a Kernel Regressor

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

P. Lopes dos Santos (Author) FEUP; Azevedo Perdicoulis, TP (Author) Other; Salgado, PA (Author) Other; Bruno Ferreira (Author) FEUP; Nuno Cruz (Author) FEUP

Conference proceedings International

Title: OCEANS 2023 - LIMERICK

OCEANS Conference

Limerick, IRELAND, JUN 05-08, 2023

Indexing

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Other information

Authenticus ID: P-00Z-5WF

DOI: 10.1109/oceanslimerick52467.2023.10244557

Abstract (EN): A kernel regressor to estimate a six-degree-of-fredoom non linear model of an autonomous underwater vehicle is proposed. Although this estimator assumes that the model coefficients are linear combinations of basis functions, it circumvents the problem of specifying the basis functions by using the kernel trick. The Gaussian radial basis function is the chosen kernel, with the Kernel matrix being regularized by its principal components. The variance of the Gaussian radial basis function and the number of principal components are hyper-parameters to be determined by the minimisation of a final prediction error criterion and using the training data. A simulated autonomous underwater vehicle is proposed was used as case study.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5