Frame Non-Orthogonality Calibration and Its Application to Transfer Alignment for Hybrid Inertial Navigation Systems

Hybrid inertial navigation systems (HINSs) integrate physical, stabilized platforms, strapdown attitude algorithms, and rotation modulation technology, offering unique performance advantages. However, the non-ideal orthogonality of the gimbal frames in the rotation mechanism introduces frame non-orthogonality errors, which limit the accuracy of high-precision attitude extraction and transfer alignment. To address the limitations of existing calibration methods—such as incomplete modeling, poor noise immunity, or dependence on the initial mounting attitude—this paper establishes a complete frame non-orthogonality error model and proposes two improved calibration methods: a three-position self-calibration method and a recursive least squares (RLSs) calibration method. Monte Carlo simulations show that the three-position method is simple in principle but sensitive to sensor measurement noise (accelerometer noise and gyroscope angular random walk), whereas the RLS method recursively fuses observation information and reduces the average standard deviation of the estimated non-orthogonality angles to 0.044″, with superior stability and repeatability. On this basis, the effect of non-orthogonality angle compensation on transfer alignment accuracy is verified, showing that the improvement of horizontal misalignment angles exceeds 96%. Finally, the proposed methods are validated through physical experiments on a HINS. After compensation with the RLS method, the peak residual roll attitude error decreases from 18.53″ to 10.6″, effectively improving the overall navigation performance of the system.