Optimization-Based Velocity-Integral Sliding-Window Coarse Alignment: Attitude Error Analysis and Validation
arXiv:2606.25639
The paper presents a first‑order error propagation model for optimization‑based coarse alignment of strapdown inertial navigation systems using GNSS‑aided sliding‑window velocity‑integral observations, and validates the model with simulations and vehicle tests.
Abstract
The optimization-based alignment (OBA) approach transforms strapdown inertial navigation system (SINS) coarse alignment into a constant initial attitude estimation problem for global navigation satellite system (GNSS)-aided in-motion alignment. While existing studies mainly improve accuracy by refining attitude determination algorithms or constructing robust observation vectors, a rigorous analytical mapping from raw sensor and aiding-velocity uncertainties to attitude errors remains unavailable for fixed-length sliding-window velocity-integral OBA. To address this issue, this paper proposes a first-order attitude error propagation model. Based on the sliding-window observation model, gyroscope errors, accelerometer errors, GNSS velocity noise, and lever-arm effects are propagated to unnormalized observation-vector perturbations, which are further mapped to attitude misalignment through Davenport's q method. The model decouples systematic errors from stochastic noise and derives the corresponding deterministic attitude offsets and error covariances. Monte Carlo simulations demonstrate that the analytical model captures deterministic offsets and statistical spread, yielding standard-deviation ratios between 0.942 and 1.036 with empirical coverage above 99.4%. Vehicle field tests show that the predicted covariance envelopes bound the actual initial-attitude errors, with the maximum residual root-mean-square error (RMSE) below 0.00495 deg. These results validate the proposed model for coarse-alignment attitude error assessment.
17 pages, 6 figures, including Supplementary Material; revised version of arXiv:2606.25639v2