Simulation Study of Kalman Filter in Target Tracking Applications

This paper primarily investigates the simulation of Kalman filter applications in target tracking. We implemented Kalman filtering for tracking moving target positions through subfunctions, while the main function conducts tracking based on specific assumptions and performs Monte Carlo simulations. The specific scenario assumes: a two-coordinate radar observes a target moving on a plane, where the target moves along the x-axis with constant velocity of 15 m/s from 0-600 seconds, starting from position (-10000 m, 2000 m). The radar scan period T is set to 2 seconds, with independent x and y observations where the standard deviation of observation noise is 100 meters for both coordinates. Multiple simulations were conducted for this scenario to validate the practical effectiveness of Kalman filter in target tracking applications.

During the simulation process, we observed that Kalman filter demonstrates excellent tracking performance with accurate and reliable results. Through observation and analysis of simulation results, we conclude that: the Kalman filter algorithm can effectively solve target tracking problems while maintaining strong robustness and real-time performance when tracking moving targets. These conclusions provide valuable reference for further research and applications.

In summary, this paper conducts detailed exploration and analysis of the Kalman filter's performance and effectiveness through simulation studies in target tracking applications, while proposing solutions and suggestions for practical implementation challenges. We hope this paper proves beneficial to readers and expect more researchers to focus on and deeply investigate the applications and development of Kalman filter algorithms.