Drone Tracking Using an Innovative UKF

Abstract : This paper addresses the drone tracking problem, using a model based on the Frenet-Serret frame. A kinematic model in 2D, representing intrinsic coordinates of the drone is used. The tracking problem is tackled using two recent filtering methods. On the one hand, the Invariant Extended Kalman Filter (IEKF), introduced in [1] is tested, and on the other hand, the second step of the filtering algorithm, i.e. the update step of the IEKF is replaced by the update step of the Unscented Kalman Filter (UKF), introduced in [2]. These two filters are compared to the well known Extended Kalman Filter. The estimation precision of all three algorithms are computed on a real drone tracking problem.
Type de document :
Communication dans un congrès
3rd conference on Geometric Science of Information (GSI 2017), Nov 2017, Paris, France. 〈https://www.see.asso.fr/gsi2017〉
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-01692469
Contributeur : Marion Pilté <>
Soumis le : jeudi 25 janvier 2018 - 10:56:54
Dernière modification le : lundi 12 novembre 2018 - 10:59:15
Document(s) archivé(s) le : jeudi 24 mai 2018 - 21:49:10

Fichier

GSI_17_Pilte.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01692469, version 1

Collections

Citation

Marion Pilté, Silvere Bonnabel, Frédéric Barbaresco. Drone Tracking Using an Innovative UKF. 3rd conference on Geometric Science of Information (GSI 2017), Nov 2017, Paris, France. 〈https://www.see.asso.fr/gsi2017〉. 〈hal-01692469〉

Partager

Métriques

Consultations de la notice

163

Téléchargements de fichiers

100