 | DOCUMENTAIREIdentifiant de la fiche: http://ori.unit-c.fr/uid/unit-ori-wf-1-3701
Schéma de la métadonnée: LOMv1.0, LOMFRv1.0, SupLOMFRv1.0
Droits: gratuit
Licence creative common Paternité
"Vous êtes libre de reproduire, distribuer et communiquer cette création au public [et] de modifier cette création" à condition de citer l'auteur et le titre du document.
Description: "The following problem is treated: we consider two different measures of a random variable. These two measures yield a random result. This couple of variables is Gaussian, characterized by the mean values (m1,m2), the variances (omega2,omega1) and the correlation coefficient r. This correlation coefficient is supposed to be positive. 1. How is it conceivable to combine these two measures in order to improve the accuracy of the measured variable estimation? (A typical situation is the following: a phenomenon is measured by two different methods; the first one yields a result (the mean) with a given incertitude (the variance); the second yields a different result with a different incertitude; these two measurements are correlated and the correlation coefficient is also known.). 2. Furthermore show that this combination yields the minimal variance estimation of the measured variable."
Mots-clés libres: Kalman filter, filtre de Kalman, Gaussian variable, variable gaussienne, quadratic form, forme quadratique, optimal control, commande optimale, fuscia
Structure: linéaire
| Indice(s) Dewey: | 629.8312
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PEDAGOGIQUEType pédagogique: exercice
Granularité: leçon
Niveau: enseignement supérieur, bac+3, bac+4
Age attendu du l'utilisateur: 18+
Public cible: apprenant
Langue de l'apprenant: Anglais
TECHNIQUEType de contenu: texte
Format: Document Microsoft Word
Entrepôt d'origine: ori-oai-workflow
Identifiant: unit-ori-wf-1-3701
Type de ressource: Ressource pédagogique
RELATIONSCette ressource est basée sur : An introduction to Kalman filtering : probabilistic and deterministic approaches
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