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Underwater MEMS Gyrocompassing: A Virtual Testing Ground

Daniel Engelsman, Itzik Klein

Abstract

In underwater navigation, accurate heading information is crucial for accurately and continuously tracking trajectories, especially during extended missions beneath the waves. In order to determine the initial heading, a gyrocompassing procedure must be employed. As unmanned underwater vehicles (UUV) are susceptible to ocean currents and other disturbances, the model-based gyrocompassing procedure may experience degraded performance. To cope with such situations, this paper introduces a dedicated learning framework aimed at mitigating environmental effects and offering precise underwater gyrocompassing. Through the analysis of the dynamic UUV signature obtained from inertial measurements, our proposed framework learns to refine disturbed signals, enabling a focused examination of the earth's rotation rate vector. Leveraging recent machine learning advancements, empirical simulations assess the framework's adaptability to challenging underwater conditions. Ultimately, its contribution lies in providing a resilient gyrocompassing solution for UUVs.

Underwater MEMS Gyrocompassing: A Virtual Testing Ground

Abstract

In underwater navigation, accurate heading information is crucial for accurately and continuously tracking trajectories, especially during extended missions beneath the waves. In order to determine the initial heading, a gyrocompassing procedure must be employed. As unmanned underwater vehicles (UUV) are susceptible to ocean currents and other disturbances, the model-based gyrocompassing procedure may experience degraded performance. To cope with such situations, this paper introduces a dedicated learning framework aimed at mitigating environmental effects and offering precise underwater gyrocompassing. Through the analysis of the dynamic UUV signature obtained from inertial measurements, our proposed framework learns to refine disturbed signals, enabling a focused examination of the earth's rotation rate vector. Leveraging recent machine learning advancements, empirical simulations assess the framework's adaptability to challenging underwater conditions. Ultimately, its contribution lies in providing a resilient gyrocompassing solution for UUVs.
Paper Structure (16 sections, 37 equations, 7 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 37 equations, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Gyrocompassing
  4. Gyroscope Measurement Model
  5. Rigid Body Kinematics
  6. Rigid Body Dynamics
  7. Proposed Approach
  8. The Big Picture
  9. Simulated Dynamics
  10. A Learning-Based Approach
  11. Empirical Assessment
  12. Conclusions
  13. Second-Order Ordinary Differential Equation
  14. Mass-Inertia Matrix
  15. Restoring forces and moments
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Diagrammatic representation of the transformation between ECEF and navigation reference frames, using the latitude, $\phi$, and longitude, $\lambda$ angles.
  • Figure 2: Relationship between the navigation frame and UUV body reference frames.
  • Figure 3: Conceptual flow of the proposed setup.
  • Figure 4: Simulation of the characteristic excitation profiles: impulse (top), step (middle), and sinusoidal (bottom) functions.
  • Figure 5: Spectral response to unit input $\boldsymbol{\tau}(t)$.
  • ...and 2 more figures