Equation (Simplified): Predicted State = System Model * Previous State
| Parameter | What it means | If too high | If too low | | :--- | :--- | :--- | :--- | | (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). | Equation (Simplified): Predicted State = System Model *
% --- 4. RUN THE FILTER LOOP --- for k = 1:n % ----- PREDICT STEP ----- x_pred = F * x_est; P_pred = F * P_est * F' + Q; | Filter trusts noisy spikes (jittery output)