Comparison of sigma-point filters for state estimation of diabetes models

In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.