DIeSEL: Distributed self-localization of a network of underwater vehicles
Published in OCEANS 2017 - Anchorage, 2017
Recommended citation: Cláudia Soares, Pusheng Ji, João Gomes, António Pascoal, "DIeSEL: Distributed self-localization of a network of underwater vehicles." OCEANS 2017 - Anchorage, 2017.
Abstract
How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with Gaussian noise, and we obtain a provably convergent, accurate and distributed positioning algorithm that outperforms the extended Kalman filter, a standard centralized solution for this problem.
BibTeX
@inproceedings{Ji2017,
author = "Soares, Cláudia and Ji, Pusheng and Gomes, João and Pascoal, António",
booktitle = "OCEANS 2017 - Anchorage",
title = "DIeSEL: Distributed self-localization of a network of underwater vehicles",
year = "2017",
pages = "pp. 1-6",
note = "citations: 2",
abstract = "How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with Gaussian noise, and we obtain a provably convergent, accurate and distributed positioning algorithm that outperforms the extended Kalman filter, a standard centralized solution for this problem.",
abstractsource = "https://arxiv.org/abs/1709.08746",
abstractsourcelabel = "Author preprint abstract",
preprinturl = "https://arxiv.org/abs/1709.08746",
organization = "IEEE"
}