A project demonstrating how probabilistic filtering can recover smooth motion trajectories from noisy position measurements. A 2D Kalman Filter combines a constant-velocity motion model with noisy observations, producing noise-reduced trajectories and accurate velocity estimates. This improves tracking stability in robotics and navigation tasks.