MATLAB Code Implementation for UKF Simulation

This article provides a detailed discussion of UKF simulation implementation with two demonstration programs targeting one-dimensional and two-dimensional tracking scenarios. The demonstrations feature thoroughly commented MATLAB code that compares UKF with Extended Kalman Filter (EKF), effectively illustrating UKF's superior performance in nonlinear estimation problems through side-by-side algorithm implementation and result visualization. The article further conducts in-depth analysis of UKF implementation details, covering critical aspects such as measurement noise configuration, dynamic model selection, and state estimator design. The code examples demonstrate practical implementation techniques for covariance matrix manipulation, sigma point generation, and nonlinear transformation handling. Additionally, the article explores UKF applications in other domains like robotic navigation and autonomous vehicles, showing how the core algorithm can be adapted for different nonlinear systems. Notably, this work focuses on explaining UKF concepts and implementation methodologies while providing substantial code examples and ready-to-run demonstration programs. The MATLAB implementations include key functions for unscented transformation, state prediction, and measurement update, serving as practical learning resources for better understanding and mastering UKF applications in real-world scenarios.