Lightweight LSTM-Enhanced In-Flight Alignment for SINS/GPS Integrated Navigation

Accurate in-flight alignment of strapdown inertial navigation systems (SINSs) is essential for reliable navigation under dynamic conditions. However, conventional SINS/GPS Kalman filtering (KF) is susceptible to model inaccuracies, uncertain noise statistics, and GPS outages, which may degrade alignment performance. To address these challenges, this paper proposes a lightweight KF–LSTM collaborative alignment method based on GPS measurement availability. Under normal GPS conditions, a long short-term memory (LSTM) network extracts temporal features from historical SINS/GPS position and velocity differences to refine KF misalignment angle estimates through output-level correction. During GPS outages, an independently trained LSTM generates misalignment-angle pseudo-measurements using inertial measurements and predicted navigation error states to assist KF updates. Upon GPS recovery, conventional measurement updates are resumed, followed by reactivation of the output-level correction mechanism. A lightweight LSTM architecture is selected based on accuracy–complexity analysis. Simulation and vehicle experiments are conducted to evaluate the proposed method against conventional and learning-assisted filtering approaches. The results demonstrate the feasibility of the proposed collaborative framework and its potential to improve alignment performance under varying GPS conditions.