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Learning-based underwater heading determination

Published on:

8 February 2024

Primary Category:

Systems and Control

Paper Authors:

Daniel Engelsman,

Itzik Klein


Key Details

Proposes learning framework for robust underwater gyrocompassing

Learns to extract earth's rotation rate signal for heading estimation

Mitigates effects of ocean currents and disturbances on UUVs

Assesses performance via simulations of challenging conditions

Contributes a resilient heading solution for autonomous vehicles

AI generated summary

Learning-based underwater heading determination

This paper introduces a machine learning framework to enable precise determination of heading angle for unmanned underwater vehicles. It focuses on mitigating environmental disturbances that degrade standard gyrocompassing methods. By analyzing inertial measurements, the framework learns to extract earth's rotation rate vector, even in dynamic conditions. Simulations assess its adaptability to challenges of underwater navigation.

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