Source Code for Simulation Study of Kalman Filter Application in Target Tracking

This paper presents the application of Kalman Filter in target tracking and provides corresponding simulation study source code. The Kalman Filter is an optimal filtering algorithm suitable for linear systems, capable of estimating and eliminating system noise to achieve more accurate predictions of target positions and trajectories. In this study, we explain the fundamental principles of Kalman Filter and demonstrate its implementation in target tracking scenarios. The algorithm implementation typically involves two main phases: prediction (using state transition matrices) and update (incorporating measurement corrections). We provide practical examples to illustrate the algorithm's application and performance metrics. Additionally, we share our complete simulation source code, which includes functions for state initialization, covariance matrix handling, and iterative filtering processes, enabling readers to gain deeper understanding of Kalman Filter's practical implementation in target tracking applications.