For a newcomer, those matrices are terrifying. This is where Phil Kim’s philosophy shines. He doesn’t start with math. He starts with a story —often a falling ball or a moving car—and then builds intuition.
% Run Kalman filter for k = 1:length(measurements) % Prediction x = A x; P = A P*A' + Q; For a newcomer, those matrices are terrifying
% 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)); He starts with a story —often a falling
And now you see the connection to : from smoothing your morning run data to stabilizing the movie you watch at night, the Kalman filter is there. Quiet. Efficient. Elegant. Efficient
% Update (correction) K = P*H'/(H*P*H' + R); % Kalman gain x = x + K*(measurements(k) - H*x); P = (eye(2) - K*H)*P;
Here is the essence of what you’ll learn to code (based on Kim’s style):