Kalman Filter For Beginners With Matlab Examples Download Top

% State: [position; velocity]
x_est = [0; 1];          % initial guess
P_est = eye(2);          % initial uncertainty
% Matrices
A = [1 dt; 0 1];         % position = pos + vel*dt, velocity constant
H = [1 0];               % we measure only position
Q = [0.01 0; 0 0.01];    % small process noise
R = measurement_noise^2; % measurement noise variance

We take a sensor measurement. We compare it to our prediction. % State: [position; velocity] x_est = [0; 1];

The difference between a perfect filter and a useless one is tuning Q and R. We take a sensor measurement

| Parameter | What it means | If too high | If too low | | :--- | :--- | :--- | :--- | | R (Measurement Noise) | Trust in sensor. High R = sensor is bad. | Filter ignores measurements (slow, drifts). | Filter trusts noisy spikes (jittery output). | | Q (Process Noise) | Trust in model. High Q = model is uncertain. | Filter jumps to every measurement (noisy). | Filter ignores real changes (lags behind truth). | The difference between a perfect filter and a

Beginner’s rule of thumb: Start with R as the variance of your sensor’s noise (measure it). Start with Q as a small diagonal matrix. Then tweak.