A Symmetry-Based Strapdown Attitude Update Algorithm Suitable for Low-Dynamic Motion

The attitude update algorithm is the core component determining the navigation accuracy of strapdown INS, directly affecting velocity and position solution precision. Existing algorithms were designed typically based on first-order Taylor expansions of the rotation vector differential equation, inevitably introducing approximation errors. To obtain precise attitude information under low-dynamic motion, a symmetry-based strapdown attitude update algorithm is proposed. An auxiliary frame is constructed in which the angular-rate vector becomes constant. This transformation exploits the rotational symmetry inherent in rigid-body rotation. The attitude update reduces to a steady rotation about a fixed axis. Consequently, the attitude update in this frame can be performed without approximation errors. The performance of the proposed algorithm under low-dynamic motion was sufficiently compared with that of the traditional algorithms in the simulation. The simulation results demonstrate that the proposed method outperforms traditional ones with an accuracy improvement of five orders of magnitude, validating its suitability for low-dynamic motion.