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Ref.: IEEE Journal of Oceanic Engineering Vol.42(3), pp.603-618
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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