Monte Carlo Method for Simulation Analysis of Tracking Filter

In this paper, we consider a target moving horizontally with initial position coordinates (-2000 meters, 500 meters) and a constant velocity of 10 meters/second. We implemented a tracking filter and conducted 100 simulation runs using the Monte Carlo method, with each simulation utilizing a scanning period measured in seconds and operating within a range specified in meters. The simulation incorporates kinematic modeling of target motion and tracking algorithms that process measurement data with statistical noise. Our analysis demonstrates that this filter can accurately track targets in dynamically changing environments, thereby improving tracking precision. The implementation typically involves Kalman filter variants for state estimation and covariance propagation, with Monte Carlo runs providing statistical performance metrics like RMS error. In practical applications, this approach can be employed for tracking aircraft, drones, and other moving objects, delivering more precise positional information that supports advancements in aerospace technology and geographic information systems.