State estimation and fault detection using box particle filtering with stochastic measurements
Tipo de documentoTexto en actas de congreso
Fecha de publicación2015
Condiciones de accesoAcceso abierto
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.
CitaciónBlesa, J., Le Gall, F., Jauberthie, C., Travé-Massuyès, L. State estimation and fault detection using box particle filtering with stochastic measurements. A: International Workshop on Principles of Diagnosis. "DX 2015 - 26th International Workshop on Principles of Diagnosis, 31 August-1 Septembrer, Paris (France)". Paris: 2015, p. 67-73.