Research on GPS/INS Integrated Navigation Data Fusion Method of Micro-Aerial Vehicle Based on Indirect Kalman Filter

With the rapid expansion of Micro Aerial Vehicles (MAVs) in unmanned aerial systems, autonomous navigation, and intelligent transportation, the demand for high-precision navigation has increased significantly. However, Inertial Navigation Systems (INS) suffer from long-term drift errors, while Global Navigation Satellite Systems (GPS) are vulnerable to environmental disturbances, making single-sensor solutions insufficient for high-accuracy navigation. To address these limitations, this study proposes a GPS/INS integrated navigation fusion method based on the Indirect Kalman Filter (IKF). INS is utilized for high-frequency state prediction, while GPS provides low-frequency position corrections, ensuring precise and lowlatency state estimation. The research establishes INS kinematics and GPS observation models, implements IKF filtering through error state modeling, and evaluates the effects of different noise levels and GPS update intervals through simulations. Experimental results demonstrate that the IKF method effectively mitigates INS drift errors and maintains stable navigation performance even under low GPS update frequency conditions. This work not only contributes to improving MAV navigation accuracy and robustness but also provides a reliable solution for applications requiring high-precision positioning in challenging environments, particularly in fields like intelligent manufacturing and mechatronics, offering broad application potential.