% Run Kalman filter estimated_positions = zeros(size(measurements)); for k = 1:length(measurements) % Predict x = A * x; P = A * P * A' + Q;
% Filter est_pos = zeros(size(t)); for k = 1:length(t) % Predict x = A * x; P = A * P * A' + Q; kalman filter for beginners with matlab examples download
% Plot results plot(0:dt:50, true_position, 'g-', 'LineWidth', 2); hold on; plot(0:dt:50, measurements, 'rx'); plot(0:dt:50, estimated_positions, 'b--', 'LineWidth', 2); legend('True', 'Noisy GPS', 'Kalman Estimate'); xlabel('Time (s)'); ylabel('Position (m)'); title('Kalman Filter for Constant Velocity'); grid on; % Filter est_pos = zeros(size(t))
State = [position; velocity; acceleration] % Plot results plot(0:dt:50
% Update K = P * H' / (H * P * H' + R); x = x + K * (measurements(k) - H*x); P = (eye(3) - K*H) * P;
est_pos(k) = x(1); end