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.