Sonar localization of an autonomous underwater vehicle

Approved for public release; distribution is unlimited. === Two different algorithms to navigate an AUV within a charted environment are presented. They use sonar returns and a local map together with the dynamic model to estimate the vehicle's position and acceleration at all times. Kalman fil...

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Bibliographic Details
Main Author: Percin, Enis.
Other Authors: Cristi, Roberto
Language:en_US
Published: Monterey, California. Naval Postgraduate School 2014
Online Access:http://hdl.handle.net/10945/39730
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-397302015-01-07T04:02:55Z Sonar localization of an autonomous underwater vehicle Percin, Enis. Cristi, Roberto Titus, Harold A. Naval Postgraduate School (U.S.) Department of Electrical and Computer Engineering Approved for public release; distribution is unlimited. Two different algorithms to navigate an AUV within a charted environment are presented. They use sonar returns and a local map together with the dynamic model to estimate the vehicle's position and acceleration at all times. Kalman filtering techniques are used to compute the estimates. The main difficulty is the presence of uncharted obstacles, which are identified by the potential function algorithm. Results show that first algorithm works in an environment without obstacles. Results from the application of the potential function algorithm in a pool using Tritech ST725 high resolution sonar show the feasibility and robustness of the potential function approach to the navigation problem. 2014-03-26T23:23:01Z 2014-03-26T23:23:01Z 1993-12 Thesis http://hdl.handle.net/10945/39730 en_US Copyright is reserved by the copyright owner Monterey, California. Naval Postgraduate School
collection NDLTD
language en_US
sources NDLTD
description Approved for public release; distribution is unlimited. === Two different algorithms to navigate an AUV within a charted environment are presented. They use sonar returns and a local map together with the dynamic model to estimate the vehicle's position and acceleration at all times. Kalman filtering techniques are used to compute the estimates. The main difficulty is the presence of uncharted obstacles, which are identified by the potential function algorithm. Results show that first algorithm works in an environment without obstacles. Results from the application of the potential function algorithm in a pool using Tritech ST725 high resolution sonar show the feasibility and robustness of the potential function approach to the navigation problem.
author2 Cristi, Roberto
author_facet Cristi, Roberto
Percin, Enis.
author Percin, Enis.
spellingShingle Percin, Enis.
Sonar localization of an autonomous underwater vehicle
author_sort Percin, Enis.
title Sonar localization of an autonomous underwater vehicle
title_short Sonar localization of an autonomous underwater vehicle
title_full Sonar localization of an autonomous underwater vehicle
title_fullStr Sonar localization of an autonomous underwater vehicle
title_full_unstemmed Sonar localization of an autonomous underwater vehicle
title_sort sonar localization of an autonomous underwater vehicle
publisher Monterey, California. Naval Postgraduate School
publishDate 2014
url http://hdl.handle.net/10945/39730
work_keys_str_mv AT percinenis sonarlocalizationofanautonomousunderwatervehicle
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