Kalman Filter For Beginners With | Matlab Examples Pdf

% Initial state x_true = [0; 1]; % start at 0, velocity 1 x_hat = [0; 0]; % initial guess P = eye(2); % initial uncertainty

% Run Kalman filter x_hat_log = zeros(2, num_steps); for k = 1:num_steps % Predict x_pred = A * x_hat; P_pred = A * P * A' + Q; kalman filter for beginners with matlab examples pdf

x_hat_log(:,k) = x_hat; end

% Update K = P_pred * H' / (H * P_pred * H' + R); x_hat = x_pred + K * (measurements(k) - H * x_pred); P = (eye(2) - K * H) * P_pred; % Initial state x_true = [0; 1]; %

% Generate noisy measurements num_steps = 50; measurements = zeros(1, num_steps); for k = 1:num_steps x_true = A * x_true; % true motion measurements(k) = H * x_true + sqrt(R)*randn; % noisy measurement end % Initial state x_true = [0

% Noise covariances Q = [0.01 0; 0 0.01]; % process noise (small) R = 1; % measurement noise (variance)