Target Tracking Using Kalman Filter with MATLAB Implementation

This article presents a comprehensive guide on implementing target tracking using Kalman filter, accompanied by complete MATLAB source code. The Kalman filter is a widely-used state estimation method that operates through continuous prediction and correction cycles to enhance tracking accuracy and stability. We will delve into the fundamental principles of Kalman filtering, covering both the prediction step (where the system state is projected forward) and the update step (where measurements are incorporated to refine the estimate). The implementation includes key mathematical components such as the state transition matrix, measurement matrix, process noise covariance, and measurement noise covariance. Our MATLAB code demonstrates practical implementation aspects including initialization procedures, recursive filtering loops, and visualization of tracking results. Additionally, we provide real-world application examples showcasing how the Kalman filter handles various tracking scenarios, such as linear motion prediction and measurement fusion from multiple sensors. These examples help readers understand practical implementation challenges and performance characteristics in different operational environments.