Towards learning-based gyrocompassing

Inertial navigation systems (INS) are foundational to navigation in both manned and autonomous platforms, with overall performance critically dependent on accurate initial alignment. This process, typically performed while stationary, establishes a reference orientation for subsequent navigation. However, traditional gyrocompassing methods often fail with low-performance gyroscopes due to limited sensitivity to Earth’s rotation rate. To overcome these limitations, we present a deep learning-based framework that significantly improves heading estimation using mid-tier inertial sensors. By learning to model and correct sensor errors, our method enables accurate gyrocompassing without reliance on extended filtering or long stationary periods. Theoretical analysis and experimental validation reveal a tenfold reduction in waiting times and over 50% lower alignment errors, substantially narrowing the gap between consumer-grade and tactical-grade systems. This establishes a new lower error bound for affordable gyros and offers a practical path towards scalable, high-precision navigation.