Kalman Filter For Beginners With Matlab - Examples Download Top Extra Quality

% Measurement Noise (Uncertainty of the sensor) R = 25;

Happy filtering!

% Model matrices (Constant velocity) F = [1, dt; 0, 1]; % State transition matrix H = [1, 0]; % Measurement matrix (we only measure position) Q = [0.01, 0; 0, 0.01]; % Process noise (small, trust model) R = measurement_noise_std^2; % Measurement noise (variance) % Measurement Noise (Uncertainty of the sensor) R

% --- Kalman Filter Initialization --- x_est = [0; 0]; % Initial state estimate P = [10 0; 0 10]; % Initial estimate covariance (high uncertainty) x_est_hist = zeros(2, N); % Process noise (small