Extended Kalman filter applied in the navigation of an AUV

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Published: 2015-06-30
DOI: https://doi.org/10.17163/ings.n13.2015.02
Keywords:
AUV, extended Kalman filter, sensor fusion, inertial navigation, navigation system, underwater vehicle

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Persing Junior Cárdenas Vivanco
Ettore Apolonio de Barros

Abstract

This work deals with the navigation problem of an autonomous underwater vehicle. Two state estimators are proposed like solution, using sensor fusion based in Extended Kalman Filter. The state estimators use measures of the following sensors: an inertial measurement unit, a Doppler effect velocity sensor, a depth sensor and a compass. The first state estimator, estimate the attitude independently of the velocity and depth estimation. In the second estimator, a coupling in velocity and attitude equations is considerate in the Extended Kalman Filter. To design and test the proposed state estimators, was employed the database of the Pirajuba autonomous underwater vehicle, This database contains the record of the vehicle sensors during sea tests. The results of a numeric simulation with this database validate the proposed state estimators in this work. Finally was made a comparative analysis of these state estimators.

Article Details

Issue
No. 13 (2015): January / June
Section
Scientific Paper

 

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Author Biographies

Persing Junior Cárdenas Vivanco

Máster en Ingeniería de Control y Automatización Mecánica, Bachiller en Ingeniería Física. Actualmente, realiza un doctorado en Ingeniería de Control y Automatización Mecánica en la Escuela Politécnica de la Universidad de São Paulo

Ettore Apolonio de Barros

Posdoctorado en la Universidad de Tokio e Instituto Superior Técnico de la Universidad Técnica de Lisboa, Doctor en Arquitectura Naval e Ingeniería Oceánica por la Universidad de Tokio, Graduado y Máster en Ingeniería Naval por la Universidad de São Paulo. Actualmente es profesor asociado del departamento de Ingeniería Mecatrónica y Sistemas Mecánicos de la Escuela Politécnica de la Universidad de São Paulo.