The random error of fiber optic gyros is a critical factor affecting their measurement accuracy. However, the statistical characteristics of these errors exhibit time-varying properties, which degrade model fidelity and consequently impair the performance of random error suppression algorithms. To address these issues, this study first proposes a recursive dynamic Allan variance calculation method that effectively mitigates the poor real-time performance and spectral leakage inherent in conventional dynamic Allan variance techniques. Subsequently, the recursive dynamic Allan variance is integrated with the process variance estimation of Kalman filtering to construct a dual-adaptive Kalman filter capable of autonomously switching and adjusting between model parameters and noise variance. Finally, both static and dynamic validation experiments were conducted to evaluate the proposed method. The experimental results demonstrate that, compared to existing algorithms, the proposed approach significantly enhances the suppression of angular random walk errors in fiber optic gyros.

In high-precision inertial navigation systems, suppressing the random errors of a fiber-optic gyroscope is of great importance. However, the traditional rule-based autoregressive moving average modeling method, when applied in Kalman filtering considering colored noise, presents inherent disadvantages in principle, including inaccurate state equations and difficulties in state dimension expansion. To this end, the noise characteristics in the fiber-optic gyroscope signal are first deeply analyzed, a random error model form is clarified, and a new model-order determination criterion is proposed to achieve the high-precision modeling of random errors. Then, based on the effective suppression of the angle random walk error of the fiber-optic gyroscope, and combined with the linear system equation of its colored noise, an adaptive Kalman filter based on noise-spectrum information decoupling is designed. This breaks through the principled limitations of traditional methods in suppressing colored noise and provides a scheme for modeling and suppressing fiber-optic gyroscope random errors under static conditions. Experimental results show that, compared with existing methods, the initial alignment accuracy of the proposed method based on 5 min data of fiber-strapdown inertial navigation is improved by an average of 48%.