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How can teams of artificial agents localize and position themselves inGPS-denied environments? How can each agent determine its position frompairwise ranges, own velocity, and limited interaction with neighbors? Thispaper addresses this problem from an optimization point of view: we directlyoptimize the nonconvex maximum-likelihood estimator in the presence of rangemeasurements contaminated with Gaussian noise, and we obtain a provablyconvergent, accurate and distributed positioning algorithm that outperforms theextended Kalman filter, a standard centralized solution for this problem.

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