Towards Learning-Based Gyrocompassing

TL;DR

This paper introduces a deep learning approach to improve initial alignment in inertial navigation by compensating for low-performance gyroscope errors, enabling accurate gyrocompassing without prolonged filtering.

Contribution

It presents a novel deep learning framework that enhances low-performance gyroscope accuracy for initial alignment, reducing reliance on additional sensors and filtering.

Findings

Deep learning effectively compensates gyro errors.

Achieves accurate gyrocompassing with low-performance gyroscopes.

Establishes a new lower error bound for initial alignment.

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.