Localization of AUVs Using Higher Order Sliding Mode Observer and Extended Kalman Filter

The localization of Autonomous Underwater Vehicles (AUVs) is a challenging problem compared to the one of ground vehicles, given the impossibility of using Global Positioning System (GPS) in undersea environments. In this paper, we propose Higher Order Sliding Mode (HOSM) observer, compared to Extended Kalman Filter (EKF), for the self-localization of an AUV with acoustic positioning. Employing bearing, elevation and depth difference measurements with respect to a surface vessel, the designed observers are able to estimate the North and East positions and the heading of the underwater vehicle. Both the HOSM observer and the EKF are then integrated with a dynamic surface controller to address the trajectory tracking problem of the AUV. Simulations carried out in Matlab/Simulink compare the performances of the two approaches.

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Publication Details

Published
2025-03-02
DOI
https://doi.org/10.1109/ut61067.2025.10947421
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
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article

Localization of AUVs Using Higher Order Sliding Mode Observer and Extended Kalman Filter

Karl D. von Ellenrieder, Sara Gomiero
Inertial Sensor and Navigation
article

Localization of AUVs Using Higher Order Sliding Mode Observer and Extended Kalman Filter

Karl D. von Ellenrieder, Sara Gomiero
article en

Abstract

The localization of Autonomous Underwater Vehicles (AUVs) is a challenging problem compared to the one of ground vehicles, given the impossibility of using Global Positioning System (GPS) in undersea environments. In this paper, we propose Higher Order Sliding Mode (HOSM) observer, compared to Extended Kalman Filter (EKF), for the self-localization of an AUV with acoustic positioning. Employing bearing, elevation and depth difference measurements with respect to a surface vessel, the designed observers are able to estimate the North and East positions and the heading of the underwater vehicle. Both the HOSM observer and the EKF are then integrated with a dynamic surface controller to address the trajectory tracking problem of the AUV. Simulations carried out in Matlab/Simulink compare the performances of the two approaches.

Free University of Bozen-Bolzano (IT)
Openalex Percentile: Top 91%
Inertial Sensor and Navigation
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