Kalman Filter Matlab File

% Update K = P*H' / (H*P*H' + R); x = x + K*(meas(k) - H*x); P = (eye(2) - K*H)*P;

for k = 1:length(t) % Measurement Update (Correct) K = P*C' / (C*P*C' + R); x = x + K*(y(k) - C*x); P = (eye(size(P)) - K*C)*P; % Time Update (Predict) x = A*x + B*u(k); P = A*P*A' + Q; end Use code with caution. 3. Built-in Functions and Toolboxes kalman filter matlab

for k = 1:N % --- Simulate True System --- % (In real life, we don't know x_true, we only get z_meas) x_true = A * x_true; x_true_hist(:, k) = x_true; % Update K = P*H' / (H*P*H' +

% 2. UPDATE % Calculate Kalman Gain S = H * P_pred * H' + R; % Innovation covariance K = P_pred * H' / S; % Kalman Gain UPDATE % Calculate Kalman Gain S = H

% Update estimate with measurement y_tilde = z_meas(k) - H * x_pred; % Innovation (Residual) x_est = x_pred + K * y_tilde;