IANet: A Deep Learning Inertial Navigation Initial Alignment Framework

Focusing on the initial alignment problem of inertial navigation system (INS) when GNSS-denied, we propose a deep learning inertial navigation initial alignment framework (IANet), construct an INS initial alignment model when GNSS-denied, and design a data-driven velocity estimator. According to INS principle, a X Temporal Convolutional Networks (XTCN) is proposed, which takes inertial device data as network input and velocity increment as network output. XTCN fully exploits the ability of feature extraction and information association in TCN residual module, deeply explores the coupling relationship between inertial device data and velocity increment, and effectively improves velocity estimation accuracy. IANet provides effective parameter information reference for INS initial alignment when GNSS-denied, which improves initial alignment accuracy. Simulation and experiment results show that XTCN can not only ensure that INS can independently and effectively complete the initial alignment when GNSS-denied, but also improve the yaw angle alignment accuracy by 56%.