This solved assignment focuses on estimating the position of a bridge crane using a Kalman filter. The provided MATLAB code implements the filter, incorporating measurements from a simulated sensor and process noise. The assignment explores key concepts like state estimation, covariance update, and the effect of measurement noise. Results are visualized through plots showing the estimated position compared to the actual position and the evolution of the covariance matrix.