Implementation of Inertial Navigation and GPS Data Fusion

In this application, we implement the fusion of inertial navigation and GPS data using Kalman filtering with an open-loop control approach. The Kalman filter serves as a crucial statistical estimation method that predicts system state variables and optimally combines them with sensor measurements to achieve more accurate state estimation. This algorithm typically involves two main stages: prediction (using system dynamics) and update (incorporating sensor measurements). The open-loop control method directly governs system outputs without requiring feedback controllers, which simplifies the implementation structure. Through this integrated approach, we can obtain more precise position information, thereby enhancing navigation performance. In practical implementation, key functions would include sensor data acquisition, coordinate transformation, state prediction models, and covariance matrix updates. Therefore, this application leverages both Kalman filtering and open-loop control to achieve robust inertial navigation and GPS data fusion, resulting in significantly improved navigation accuracy.