  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.
