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.

翻译：惯性导航系统广泛应用于有人与无人平台。在系统运行前最关键的任务之一是精确确定其在静止状态下的初始对准，这构成了整个惯导系统运行轨迹的基础。虽然低性能加速度计能轻易测定横滚角和俯仰角（调平），但利用低性能陀螺仪确定航向角（陀螺罗经对北）在没有额外传感器的情况下仍具挑战性。这是由于地球自转角速度信号微弱，常常被陀螺仪自身噪声所淹没。为弥补这一缺陷，本研究提出了一种实用的深度学习框架，旨在有效补偿低性能陀螺仪的固有误差。由此获得的能力使陀螺罗经对北成为可能，从而无需后续长时间的滤波阶段（精对准）。通过理论推导与实验验证，我们证明改进后的初始条件建立了新的更低误差界，使低成本陀螺仪向高端战术任务应用迈进了关键一步。