Extended Kalman Filter for Target Tracking Simulation

This paper provides a detailed implementation guide for target tracking simulation using Extended Kalman Filter (EKF). We begin with an overview of EKF fundamentals, explaining how this nonlinear filtering technique extends standard Kalman Filter through Taylor series linearization of system models. The implementation demonstrates EKF's application to tracking moving targets such as aircraft or vehicles, where the algorithm handles nonlinear motion models and measurement equations. Our simulation includes state prediction using system dynamics models and measurement update phases that incorporate sensor observations. The code implementation features Jacobian matrix calculations for linearization at each time step, with covariance propagation maintaining uncertainty estimates. We conduct comprehensive error analysis using performance metrics like Root Mean Square Error (RMSE) to evaluate method accuracy and reliability. Finally, we propose enhancement directions including adaptive noise tuning, multiple model approaches, and computational optimization to further improve target tracking performance and simulation efficiency.