Inertial Navigation System State Estimation Using Kalman Filter Algorithm

In this code sharing, the author provides source code that utilizes the Kalman filter algorithm to estimate states of an inertial navigation system. Although concise, this algorithm can be implemented in numerous practical applications, such as navigation and flight control systems in the aviation field. The Kalman filter algorithm, based on Bayesian theorem, enhances prediction accuracy through iterative state estimation. The implementation requires understanding of both system dynamic models and observation models, typically involving state transition matrices and measurement matrices. Key functions likely include prediction steps (time update) using system dynamics and correction steps (measurement update) incorporating sensor measurements. Therefore, when applying this code, certain adjustments and optimizations are necessary based on specific system parameters and noise characteristics. We appreciate the author's contribution, enabling us to gain deeper insights into the practical application and learning of Kalman filter algorithms.