  How to self-localize large teams of underwater nodes using only noisy range
measurements? How to do it in a distributed way, and incorporating dynamics
into the problem? How to reject outliers and produce trustworthy position
estimates? The stringent acoustic communication channel and the accuracy needs
of our geophysical survey application demand faster and more accurate
localization methods. We approach dynamic localization as a MAP estimation
problem where the prior encodes dynamics, and we devise a convex relaxation
method that takes advantage of previous estimates at each measurement
acquisition step; The algorithm converges at an optimal rate for first order
methods. LocDyn is distributed: there is no fusion center responsible for
processing acquired data and the same simple computations are performed for
each node. LocDyn is accurate: experiments attest to a smaller positioning
error than a comparable Kalman filter. LocDyn is robust: it rejects outlier
noise, while the comparing methods succumb in terms of positioning error.
