Drone Tracking Using an Innovative UKF

Marion Pilté (1, 2) , Silvere Bonnabel (2) , Frédéric Barbaresco (1)

1 Thales Air Systems
2 CAOR - Centre de Robotique

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.

Mots clés: Tracking; Geometric Estimation; Kalman Filtering

Domaines: Mathématiques [math]; Mathématiques appliquées

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. ⟨hal-01692469⟩