Target Tracking Using Extended Kalman Filter Algorithm for Maneuvering Motion

This paper proposes a maneuvering target model consisting of three distinct motion phases: initial uniform linear motion, uniform circular motion, and final return to uniform linear motion, with constant linear velocity v maintained throughout the entire process. For target localization and tracking, the Extended Kalman Filter (EKF) algorithm is implemented, which effectively handles the nonlinear characteristics of maneuvering trajectories through linearization techniques around the current state estimate. The algorithm's implementation involves two core recursive steps: prediction (using the state transition model) and update (incorporating new measurements with the Kalman gain calculation). Simulation experiments demonstrate that this model not only accurately represents practical maneuvering scenarios but also simplifies mathematical processing. The EKF algorithm maintains stable filtering performance with rapid convergence rates and high positioning accuracy, substantially improving both tracking precision for maneuvering targets and overall system responsiveness. The combined approach of this maneuvering target model with EKF filtering proves highly valuable for practical target tracking applications.