Extended Kalman Filter Toolbox

When learning and applying Kalman filtering, the Extended Kalman Filter Toolbox serves as an indispensable resource. It incorporates various filtering methods such as Unscented Kalman Filter (UKF), Particle Filter, and others, accompanied by comprehensive examples and practical application scenarios. For instance, the toolbox can be implemented to predict and optimize robot motion trajectories using state transition functions and measurement models, or to monitor and control aircraft flight status under varying meteorological conditions through sensor fusion algorithms. The toolbox features straightforward usage with MATLAB-based functions like ekf() and ukf() for initialization, prediction, and update steps, making it accessible even for beginners to quickly grasp core Kalman filtering concepts and implementation techniques. The package includes commented code examples demonstrating covariance matrix handling, Jacobian calculations for nonlinear systems, and real-time data processing workflows.