Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features
Zhuoyuan Song, Kamran Mohseni

TL;DR
This paper introduces a current-aided inertial navigation system for underwater vehicles that uses ocean current maps and flow field features to improve long-duration navigation accuracy without surfacing.
Contribution
It proposes a novel navigation framework combining flow field features with particle filtering to enhance long-term underwater positioning accuracy.
Findings
Significantly reduces dead-reckoning errors in turbulent flow conditions.
Achieves under 3% uncertainty per distance traveled in 6-hour simulations.
Demonstrates consistent performance over 24-hour missions with field data.
Abstract
A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Underwater Acoustics Research · Target Tracking and Data Fusion in Sensor Networks
