% Kalman Filter Loop for k = 1:num_samples % --- Prediction --- x_pred = A * x_est; % State prediction P_pred = A * P_est * A' + Q; % Covariance prediction
To use a Kalman filter, you must represent your system using linear equations. A. System State ( This is what we want to know (e.g., Position, Velocity).
% Measurement update step K = P_pred * H' / (H * P_pred * H' + R); x_upd = x_pred + K * (z(i) - H * x_pred); P_upd = (1 - K * H) * P_pred;
You will see a plot showing how the blue Kalman Filter line successfully ignores the red noise and tracks the black true position. 6. Key Takeaways for Beginners
