Kalman Filter For Beginners With Matlab Examples Download [extra Quality] Top Jun 2026

% --- The "Truth" (Simulation of reality) --- true_position = 100 - 0.5 * g * t.^2; % Falling from 100m true_velocity = -g * t;

In Example 1, change R from 25 to 250 and re-run. Notice how the blue line becomes but lags behind the true position. Change R to 1, and the blue line becomes almost as noisy as the red dots . This is the trade-off. % --- The "Truth" (Simulation of reality) ---

% Noisy Measurements (Position only, with noise) measurement_noise_std = 5; % Standard deviation of sensor noise measurements = true_pos + measurement_noise_std * randn(1, N); In Example 1