# Online Estimation of Ocean Current from Sparse GPS Data for Underwater Vehicles

## TL;DR

This paper introduces a Gaussian process-based EM algorithm that estimates ocean currents from sparse GPS data and dead-reckoned positions, improving underwater navigation by leveraging position drift.

## Contribution

The paper presents a novel GP-EM algorithm that exploits ocean current incompressibility to accurately estimate currents from limited GPS data for underwater vehicles.

## Key findings

- Successfully reconstructs ocean current fields in simulation
- Validates the approach on real GPS datasets
- Demonstrates potential for improved underwater navigation

## Abstract

Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process~(GP)-based expectation-maximisation~(EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.