Kalman Filter For Beginners With Matlab Examples - Download Top

stored_x = zeros(2, N);

rmse_raw = sqrt(mean((measurements - true_pos).^2)); rmse_kalman = sqrt(mean((stored_x(1,:) - true_pos).^2)); fprintf('Raw sensor RMSE: %.3f m\n', rmse_raw); fprintf('Kalman filter RMSE: %.3f m\n', rmse_kalman); stored_x = zeros(2, N); rmse_raw = sqrt(mean((measurements -

H = [1, 0]; % Measure only position Q = [0.001, 0; 0, 0.001]; % Process noise (small) R = meas_noise_std^2; % Measurement noise stored_x = zeros(2

%% Plotting figure; plot(t, true_pos, 'g-', 'LineWidth', 2); hold on; plot(t, measurements, 'r.', 'MarkerSize', 4); plot(t, stored_x(1,:), 'b-', 'LineWidth', 2); xlabel('Time (s)'); ylabel('Position (m)'); title('Tracking a Falling Object with Kalman Filter'); legend('True Position', 'Noisy Measurements', 'Kalman Estimate'); grid on; rmse_raw = sqrt(mean((measurements - true_pos).^2))

% Store results stored_x(:, k) = x_est; stored_P(:, :, k) = P_est; end

%% 1. SIMULATE THE REAL WORLD dt = 0.1; % Time step (seconds) t = 0:dt:10; % Time vector (10 seconds) N = length(t); % Number of time steps

% Storage for results stored_x = zeros(2, N); stored_P = zeros(2, 2, N);