K = P_pred * H' * inv(H * P_pred * H' + R)
% Update step innovation = z(:, i) - H * x_pred; K = P_pred * H' * (H * P_pred * H' + R)^-1; x_est = x_pred + K * innovation; P_est = (eye(2) - K * H) * P_pred; kalman filter for beginners with matlab examples download
The book covers the Linear Kalman Filter very well, and touches briefly on the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). However, if you are working on highly non-linear systems or need to implement a Particle Filter, you will need a more advanced text after finishing this one. K = P_pred * H' * inv(H *
: Based on sensor data, which is often noisy. % --- Initialize Kalman Filter --- % State:
% --- Initialize Kalman Filter --- % State: [position; velocity] x_est = [0; 9]; % Initial guess (slightly wrong velocity) P_est = [100 0; 0 10]; % High initial uncertainty
A significant value proposition of this book is the accompanying source code.
% --- Generate True Data and Measurements --- t = 0:dt:10; N = length(t); u = 0.5 * ones(1, N); % Constant acceleration input