The direct fusion of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) data using a Kalman filter is a highly effective method for enhancing navigation solution accuracy and stability. This approach distinguishes itself from indirect methods by delivering superior performance in estimation speed and precision, achieved through a simplified filter structure and reduced processing delays inherent in other integration techniques. To validate these advantages, a nonlinear Extended Kalman Filter (EKF) was implemented for a detailed analysis of parameter estimation.
A comprehensive analysis of real-world flight data confirms the significant benefits of the direct fusion method. The results demonstrate a notable increase in estimation accuracy, along with enhanced stability and robustness under adverse conditions such as signal degradation or dynamic maneuvers. This approach also demonstrated faster convergence speeds, allowing the navigation solution to stabilize more quickly. Furthermore, flight test validation confirms that this direct approach accelerates the estimation of initial INS errors. This capability leads to a more rapid determination of geographic north during the alignment phase, a critical factor for rapid system deployment. Ultimately, this study provides a comprehensive empirical validation of the direct fusion method, highlighting its practical value for modern navigation challenges.