Towards Learning-Based Gyrocompassing
CoRR(2023)
Abstract
Inertial navigation systems (INS) are widely used in both manned and
autonomous platforms. One of the most critical tasks prior to their operation
is to accurately determine their initial alignment while stationary, as it
forms the cornerstone for the entire INS operational trajectory. While
low-performance accelerometers can easily determine roll and pitch angles
(leveling), establishing the heading angle (gyrocompassing) with
low-performance gyros proves to be a challenging task without additional
sensors. This arises from the limited signal strength of Earth's rotation rate,
often overridden by gyro noise itself. To circumvent this deficiency, in this
study we present a practical deep learning framework to effectively compensate
for the inherent errors in low-performance gyroscopes. The resulting capability
enables gyrocompassing, thereby eliminating the need for subsequent prolonged
filtering phase (fine alignment). Through the development of theory and
experimental validation, we demonstrate that the improved initial conditions
establish a new lower error bound, bringing affordable gyros one step closer to
being utilized in high-end tactical tasks.
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