% Kalman filter for beginners - inspired by Phil Kim's approach dt = 1; % time step A = [1 dt; 0 1]; % state transition matrix H = [1 0]; % measurement matrix Q = [0.1 0; 0 0.1]; % process noise R = 10; % measurement noise x = [0; 0]; % initial state P = eye(2); % initial uncertainty % Simulate noisy measurements true_position = 0:dt:100; measurements = true_position + sqrt(R)*randn(size(true_position));
estimated_position(k) = x(1); end
So download the PDF (legally), fire up MATLAB, and type x = A*x . The world of recursive estimation awaits—and it is far less scary than you imagined. % Kalman filter for beginners - inspired by