Target Tracking Using Kalman Filter with Linear Motion Model

In this article, we demonstrate how to implement target tracking using the Kalman Filter. The Kalman Filter is a widely-used state estimation method that employs mathematical modeling of system dynamics and observation models to estimate and predict system states. For target tracking applications, we typically model the target's motion as a linear system, making the Kalman Filter particularly effective. The implementation involves two main steps: prediction (using system dynamics) and update (incorporating measurements). Key functions would include state transition matrix calculation, measurement matrix definition, and covariance matrix updates. Through state estimation and prediction of the target's motion, we can accurately track both position and velocity parameters, resulting in precise target tracking performance. The algorithm typically requires initializing state vectors (position and velocity) and covariance matrices, followed by recursive prediction-correction cycles where the filter gains are optimally computed to minimize estimation error.