DIeSEL: DIstributed SElf-Localization of a network of underwater vehicles

TL;DR

This paper introduces DIeSEL, a distributed algorithm for underwater vehicle networks to localize themselves accurately without GPS, using optimization of noisy range measurements, outperforming traditional methods.

Contribution

The paper presents a novel distributed localization algorithm based on direct optimization of the maximum-likelihood estimator, with proven convergence and improved accuracy over existing methods.

Findings

The proposed DIeSEL algorithm achieves higher accuracy than the extended Kalman filter.

DIeSEL is provably convergent and suitable for distributed implementation.

The method effectively handles Gaussian noise in range measurements.

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.