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Ref.: IEEE Journal of Oceanic Engineering ( Volume: PP, Issue: 99 )
Abstract:
This paper proposes a novel design for the localization system of autonomous underwater vehicles (AUV) using acoustic
signals. The solution presented exploits models based on kernel functions with two main purposes: i) to reject outliers; ii) to
correct or improve accuracy of measurements. The localization system discussed is based on well-established techniques such as
Support Vector Data Description (SVDD) and AutoAssociative Kernel Regression (AAKR) derived from machine learning theory
that utilizes heuristic models for classification and regression tasks respectively. By coupling the algorithm to the navigation
system, we seek to reduce the sensitivity of the localization scheme to the reflected acoustic waves or fluctuations of underwater
channel properties without modifying the solution used for data fusion or overloading the algorithm embedded in the vehicle. Data
collected in the field with a light underwater vehicle (LAUV) was used to demonstrate the advantages of the proposed approach.
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