Secure navigation is pivotal for several applications including autonomous vehicles, robotics, and aviation. The inertial navigation system estimates position, velocity, and attitude through dead reckoning especially when external references like GPS are unavailable. However, the three accelerometers and three gyroscopes that compose the system are exposed to various types of errors including bias errors, scale factor errors, and noise, which can significantly degrade the accuracy of navigation constituting also a key vulnerability of this system. This work aims to adopt a supervised convolutional neural network (ConvNet) to address this vulnerability inherent in inertial navigation systems. In addition to this, this paper evaluates the impact of the ConvNet layer's depth on the accuracy of these corrections. This evaluation aims to determine the optimal layer configuration maximizing the effectiveness of error correction in INS (Inertial Navigation System) leading to precise navigation solutions.

翻译：安全导航对于自动驾驶车辆、机器人技术和航空等若干应用至关重要。惯性导航系统通过航位推算法估算位置、速度和姿态，尤其是在GPS等外部参考不可用时。然而，构成该系统的三个加速度计和三个陀螺仪易受多种误差影响，包括偏置误差、比例因子误差和噪声，这些误差会显著降低导航精度，也成为该系统的关键脆弱性。本研究旨在采用监督式卷积神经网络（ConvNet）来解决惯性导航系统固有的这一脆弱性。此外，本文评估了ConvNet网络层深度对这些校正精度的影响。该评估旨在确定能最大化惯性导航系统误差校正效果、从而获得精确导航解算的最优网络层配置。