Fiber Optic Gyroscope Modeling Based on ARMA(2) Model with Kalman Filtering Algorithm

This research focuses on fiber optic gyroscope modeling using the ARMA(2) model and its signal processing through Kalman filtering algorithms. We employ the ARMA(2) model to establish a mathematical representation of fiber optic gyroscope behavior. Fiber optic gyroscopes are critical inertial navigation instruments designed to measure rotational motion. By developing this model, we can gain deeper insights into the operational principles of fiber optic gyroscopes and provide guidance for performance optimization. The implementation typically involves estimating ARMA coefficients using system identification techniques and validating the model against experimental gyroscope data. Furthermore, we apply the Kalman filtering algorithm to process and filter fiber optic gyroscope signals. The Kalman filter serves as a widely-used signal processing method that enhances signal accuracy and stability through optimal estimation. The algorithm implementation consists of two main stages: prediction (where the system state is forecasted based on previous states) and update (where measurements are incorporated to refine the state estimate). This recursive processing effectively reduces measurement noise and improves signal quality. Through this research, we aim to contribute to the fields of fiber optic gyroscope modeling and signal processing, while supporting advancements in navigation and inertial measurement applications. The combined approach of ARMA modeling and Kalman filtering provides a comprehensive framework for both theoretical analysis and practical implementation in inertial navigation systems.