Extended Kalman Filter Information Synchronous Data Fusion Algorithm

In this article, we provide a detailed explanation of implementing the Extended Kalman Filter (EKF) information synchronous data fusion algorithm. This algorithm is primarily used to fuse data from multiple sensors to achieve more accurate system state estimation. Specifically, we will explore the algorithm's principles and implementation steps, including system state prediction, Kalman gain calculation, and state estimation update. From a coding perspective, we'll discuss key functions such as the state transition matrix computation, Jacobian matrix derivation for nonlinear systems, and covariance propagation. We will also explain how to adjust algorithm parameters based on practical application scenarios for improved performance, and present application cases demonstrating the algorithm's real-world effectiveness. Through this article, readers will gain deep insights into the EKF-based information synchronous data fusion algorithm and learn to flexibly apply it in practical scenarios to enhance the accuracy and stability of system state estimation.