Extended Kalman Filter Code Implementation

This article presents an instructive example involving MATLAB simulation using the Extended Kalman Filter (EKF) algorithm. This practical demonstration illustrates how to employ the EKF algorithm for estimating system states even when subjected to external noise and disturbances. The example features MATLAB simulation of robotic motion, utilizing EKF for position and orientation estimation. We provide comprehensive explanations of EKF algorithm principles and implementation methodologies, including detailed MATLAB code ready for direct copy-paste integration into your own projects. The implementation showcases key aspects including state transition functions, measurement models, Jacobian matrix calculations, and covariance propagation. The complete code structure demonstrates initialization parameters, prediction steps using system dynamics, and correction steps incorporating sensor measurements. This example aims to enhance your understanding of EKF applications and advantages through hands-on implementation, featuring practical considerations for handling nonlinear systems and optimizing filter performance.