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

Péter Szalay, Adrienn Molnár, Márk Müller, György Eigner, Imre Rudas, Zoltán Benyó, Levente Kovács

Research output: Contribution to journalConference article

6 Citations (Scopus)


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.

Original languageEnglish
Article number6974298
Pages (from-to)2476-2481
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Issue numberJanuary
Publication statusPublished - Jan 1 2014
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: Oct 5 2014Oct 8 2014

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Comparison of sigma-point filters for state estimation of diabetes models'. Together they form a unique fingerprint.

  • Cite this