IEEE Robotics & Automation Magazine - September 2019 - 39

Dead Reckoning
of Dynamically
Positioned Ships

©ISTOCKPHOTO.COM/TRYAGING

Using an Efficient Recurrent Neural Network

By Robert Skulstad, Guoyuan Li, Thor I. Fossen,
Bjørnar Vik, and Houxiang Zhang

W

hen a ship experiences a loss of position reference systems, its navigation
system typically enters a mode known as dead reckoning (DR) to maintain
an estimate of its position. Commercial systems perform this task using a
state estimator that includes mathematical model knowledge. Such a
model is nontrivial to derive and needs tuning if the vessel's dynamic
properties change. To this end, we propose using machine learning to estimate the
horizontal velocity of the vessel without the help of position, velocity, or acceleration
Digital Object Identifier 10.1109/MRA.2019.2918125
Date of publication: 12 August 2019

1070-9932/19©2019IEEE

SEPTEMBER 2019

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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39


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IEEE Robotics & Automation Magazine - September 2019

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2019

Contents
IEEE Robotics & Automation Magazine - September 2019 - Cover1
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IEEE Robotics & Automation Magazine - September 2019 - Contents
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IEEE Robotics & Automation Magazine - September 2019 - Cover3
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