State estimation and fault detection using box particle filtering with stochastic measurements
Résumé
In this paper, we propose a box particle filtering
algorithm for state estimation in nonlinear
systems whose model assumes two types of uncertainties:
stochastic noise in the measurements
and bounded errors affecting the system dynamics.
These assumptions respond to situations frequently
encountered in practice. The proposed
method includes a new way to weight the box
particles as well as a new resampling procedure
based on repartitioning the box enclosing the updated
state. The proposed box particle filtering
algorithm is applied in a fault detection schema
illustrated by a sensor network target tracking example.
Domaines
Automatique / RobotiqueOrigine | Fichiers produits par l'(les) auteur(s) |
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