Extended Kalman Filter Implementation in MATLAB: Target Tracking Applications

In target tracking applications, the Extended Kalman Filter (EKF) serves as a widely-used method that leverages previous measurements and model predictions to estimate target states. This article presents a comprehensive MATLAB implementation of the Extended Kalman Filter, detailing the algorithmic workflow through practical code examples. The implementation covers key steps including state prediction using nonlinear motion models (typically implemented through f(x) functions), measurement updates with Jacobian matrix calculations (using jacobian() or numerical differentiation), and covariance propagation. We provide sample MATLAB code demonstrating how to initialize state vectors, define process and measurement noise matrices (Q and R), and implement the recursive prediction-correction cycle. Additionally, we discuss EKF's applications in target tracking scenarios such as nonlinear motion patterns and sensor fusion, while analyzing the method's advantages in handling nonlinear systems and limitations concerning computational complexity and linearization errors. Through this tutorial, readers will gain deeper understanding of EKF principles and practical implementation techniques, enabling improved performance in target tracking and related domains.