Implementation Process of UKF Algorithm in MATLAB

This article comprehensively explores the implementation process of the Unscented Kalman Filter (UKF) algorithm in MATLAB. UKF serves as an enhanced variant of the Extended Kalman Filter (EKF), providing superior handling of nonlinear systems through sigma point transformation rather than linearization. We will discuss UKF's fundamental principles and algorithmic workflow, including key steps such as sigma point selection, nonlinear transformation via unscented transform, and covariance update procedures. The implementation section covers MATLAB code development strategies, focusing on critical functions like chol() for covariance matrix decomposition and ode45 for nonlinear system propagation. Additionally, we provide practical MATLAB testing examples with detailed code annotations, demonstrating state estimation in nonlinear dynamic systems. These examples are particularly suitable for UKF beginners, offering hands-on experience with parameter tuning, initialization techniques, and performance evaluation metrics to deepen understanding of UKF implementation methodologies.