A Method for Online Multisensor Error Identification in RINS

To address the problem in rotational inertial navigation systems (RINS) where residual sensor calibration errors lead to limited alignment convergence speed and insufficient instantaneous output accuracy, this paper proposes a multi-sensor error online identification and compensation method based on state-expanded self-alignment. Through theoretical analysis, the propagation mechanism of sensor errors under rotational modulation is clarified. On this basis, the traditional self-alignment model is expanded, and a more complete measurement equation is constructed. The QR-RLS algorithm is employed to achieve high-precision online identification and decoupling of errors. Simulation and experimental results show that this method can effectively realize online identification of various sensor errors in RINS, reducing platform misalignment angle estimation oscillations by 70% during alignment and improving the alignment speed by more than 40%. Subsequent navigation experiments show that, after decoupling and compensating for various sensor errors using this method, the velocity and azimuth attitude oscillations within the rotation period are reduced by approximately 60% and 50%, respectively. This study provides a systematic technical approach for online identification of multi-sensor errors in RINS, while improving the convergence speed of alignment and the instantaneous output accuracy of the inertial navigation system, and has important engineering value for enhancing the performance of high-precision inertial navigation systems.