end
% 2. Innovation: measurement - predicted measurement y = measurements(k) - H * x_hat_pred;
The Kalman filter runs continuously in a two-step loop: and Update (Correct) . end % 2
Search for “Kalman filter for beginners with matlab examples download top” on MATLAB File Exchange, or visit GitHub and look for kf_beginner_kit.zip . Your future self will thank you.
Adjust these parameters to experiment:
) arrives, the filter updates its prediction. It computes the Kalman Gain (
where x is the state, P is the covariance, A is the system dynamics matrix, Q is the process noise covariance, H is the measurement model matrix, R is the measurement noise covariance, y is the measurement, and I is the identity matrix. Your future self will thank you
) to estimate where the system should be at the next time step.