Kalman Filter Matlab Direct

Tuning Q and R is everything. Too low Q → filter ignores new data; too high → noisy output.

% Simulated measurements true_pos = 0:dt:10; meas = true_pos + sqrt(R)*randn(size(true_pos)); kalman filter matlab

est_pos(k) = x(1); end

% Plot plot(true_pos, 'g-', meas, 'ro', est_pos, 'b--') legend('True', 'Noisy', 'Kalman estimate') Tuning Q and R is everything

After struggling with prediction/correction steps for a while, I implemented a basic Kalman filter for a 1D motion model in MATLAB. Sharing a clean working example. meas = true_pos + sqrt(R)*randn(size(true_pos))