The article discusses two algorithms, Extended Kalman Filter (EKF) and Particle Filter (PF), used for target tracking. EKF approximates nonlinear models using linear transformations, while PF represents probabilities with weighted particles. The study shows that PF performs better than EKF in strong nonlinear and non-Gaussian environments. In weak nonlinear and non-Gaussian environments, both algorithms have similar tracking performance, but PF has higher computational complexity.