% Kalman loop for k = 1:length(meas) % Predict x = F x; P = F P*F' + Q;
% Update K = P*H' / (H*P*H' + R); x = x + K*(meas(k) - H*x); P = (eye(2) - K*H)*P; kalman filter matlab
dt = 0.1; % time step F = [1 dt; 0 1]; % state transition H = [1 0]; % measurement matrix Q = [0.01 0; 0 0.01]; % process noise R = 0.1; % measurement noise % Initial guess x = [0; 0]; P = eye(2); % Kalman loop for k = 1:length(meas) %
% Simulated measurements true_pos = 0:dt:10; meas = true_pos + sqrt(R)*randn(size(true_pos)); P = F P*F' + Q
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