Stochastic Modeling of Interferometric Optical Gyroscopes for High-End Applications: Experimental Validation

Interferometric optical gyroscopes (IOGs) are widely used for precise rotational sensing in fields ranging from aerospace and defense to emerging autonomous platforms, exploiting the Sagnac effect to detect angular velocity with remarkable stability. Recent progress in fiber-optic and integrated photonic implementations has enabled improvements in size and performance, yet incomplete modeling continue to make complex and time-consuming the development of robust high-end systems. Here, we present a comprehensive stochastic model that unifies all major noise sources-including shot noise, thermal noise, and nonlinear Kerr effects-within a single predictive framework. We show that, when experimentally validated on a fiber-optic gyroscope (FOG) at prototype level spanning ±10 °/s; the model accurately reproduces both short-term responses and long-term drift phenomena such as bias instability and angle random walk, with discrepancies of the order of 10 %. Compared to prior methods that often treat optical components as noise-free or omit certain perturbations, our results substantially reduce the gap between theoretical estimates and real-world sensor behavior. By providing validated insights into the interplay of hardware characteristics and noise processes, our model can guide the design of next-generation IOGs with improved accuracy, stability, and potential for seamless integration into complex navigation architectures. Ultimately, this work can support the adoption of IOG technology in applications demanding both miniaturized form factors and navigation-grade precision.