# LocDyn: Robust Distributed Localization for Mobile Underwater Networks

## TL;DR

LocDyn is a distributed, robust, and accurate localization algorithm for mobile underwater networks that incorporates dynamics, rejects outliers, and outperforms Kalman filters in experiments.

## Contribution

It introduces a convex relaxation-based distributed localization method that accounts for dynamics and outlier rejection, improving accuracy and robustness over existing methods.

## Key findings

- LocDyn achieves smaller positioning errors than Kalman filters.
- The algorithm converges at an optimal rate for first order methods.
- LocDyn effectively rejects outlier noise in underwater localization.

## Abstract

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.