% Plot plot(true_pos, 'g-', meas, 'ro', est_pos, 'b--') legend('True', 'Noisy', 'Kalman estimate')
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);
Here’s a ready-to-use post for a forum, LinkedIn, or blog comment section about using the . Title: Finally got the Kalman Filter working in MATLAB – here’s what I learned kalman filter matlab
Estimate position and velocity from noisy measurements.
Happy filtering! 🔍
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)); % Plot plot(true_pos, 'g-', meas, 'ro', est_pos, 'b--')
% Update K = P*H' / (H*P*H' + R); x = x + K*(meas(k) - H*x); P = (eye(2) - K*H)*P;