Target Tracking Problem

In the field of radar data processing, the target tracking problem has significant practical applications. After detecting targets and recording their position data, radar systems need to process the measured target position data (referred to as plots) to automatically form target trajectories and predict target positions at next time steps. This paper briefly discusses the process of using Kalman filtering method for single target trajectory prediction and employs MATLAB simulation tools to evaluate experimental performance. Our implementation uses trajectory.m to generate theoretical trajectories and visualization plots, while Kalman_filter.m applies the Kalman filtering algorithm for target trajectory estimation through recursive prediction and update steps that incorporate measurement noise and process noise models. Additionally, we analyze the results from filter_result.m, which provides Kalman filtering estimation outcomes including mean estimation error and standard deviation curves that demonstrate the filter's convergence and stability properties. Through this research, we can gain deeper insights into radar target tracking problems and existing solutions, providing reference and inspiration for future studies in this domain.