Gauge Freedom Optimization for Truncation Error Reduction in Inertial Navigation

Abstract

Numerical integration plays a central role in inertial navigation systems, where sensor measurements are propagated through time to obtain orientation, velocity, and position states. The accuracy of this propagation depends on the numerical integrator type, order and step-size. Prior work showed that for second-order systems with known forcing functions, the gauge freedom in the variation of parameters technique can be exploited to reduce truncation error without modifying the integrator. However, this approach requires analytical knowledge of the forcing function, limiting its applicability in real-world systems. To address this limitation we propose the u-space methodology, a novel state mapping that generalizes the gauge freedom to systems with unknown forcing functions. The optimal gauge is derived in closed form for second-order systems and in both closed and empirical form for first-order systems. The proposed approach was evaluated through Monte Carlo simulations across four forcing functions, five sensor grades, and four Adams-Bashforth orders, as well as on a real-world inertial navigation dataset. Results show consistent error reduction across all tested conditions, with the largest gains observed in the full inertial mechanization pipeline, making the approach applicable to high-grade inertial systems, where truncation error constitutes a larger share of the error budget, and to aided low-cost systems with high-rate updates, where propagation spans only short inter-update intervals.