Kalman Filter For Beginners With Matlab Examples Download Top ✓ < Essential >

for k = 1:N % Prediction with known input x_pred = F * x_est + B * u; P_pred = F * P_est * F' + Q;

H = [1, 0]; % Measure only position Q = [0.001, 0; 0, 0.001]; % Process noise (small) R = meas_noise_std^2; % Measurement noise for k = 1:N % Prediction with known

for k = 1:N true_pos(k) = true_vel * t(k); end H = [1

%% Kalman Filter x_est = [0; 0]; % [pos; vel] P_est = eye(2) * 1; vel] P_est = eye(2) * 1

% Measurement update z = measurements(k); y = z - H * x_pred; S = H * P_pred * H' + R; K = P_pred * H' / S;

%% True dynamics (with no noise) true_pos = 0.5 * g * t.^2; % s = 0.5 g t^2 true_vel = g * t; % v = g*t

git clone https://github.com/balzer82/Kalman MATLAB.zip If you are an instructor, create a ZIP of the above scripts and host it. Here is a simple batch script (Windows) or bash (Mac/Linux) to create a zip: