Kalman Filter-Based Tracking Algorithm for Mobile Target Tracking

In this article, we present a Kalman filter-based tracking algorithm implemented using MATLAB. This algorithm can accurately track moving targets regardless of whether they remain within the field of view. The Kalman filter algorithm is a recursive estimation technique designed for state estimation in dynamic systems. In tracking applications, the Kalman filter utilizes the target's motion model and observation measurements to predict target positions and subsequently correct these predictions. The algorithm implementation requires configuring several key parameters including the target's initial position, motion state (velocity/acceleration), and observation noise characteristics. The MATLAB implementation typically involves defining state transition matrices, measurement matrices, process noise covariance, and measurement noise covariance. Key functions include predict() for state prediction and update() for measurement correction phases. We provide a straightforward implementation example that demonstrates how to initialize the Kalman filter parameters, process sequential measurements, and maintain tracking continuity. This example helps users better understand and apply Kalman filtering concepts in practical tracking scenarios, with code structure highlighting the prediction-correction cycle and noise handling mechanisms.