Publication

State estimation and fault detection using box particle filtering with stochastic measurements

Conference Article

Conference

International Workshop on Principles of Diagnosis (DX)

Edition

26th

Pages

67-73

Doc link

http://ceur-ws.org/Vol-1507/dx15paper9.pdf

File

Download the digital copy of the doc pdf document

Authors

Projects associated

Abstract

In this paper, we propose a box particle filtering algorithm for state estimation in nonlinear systems whose model assumes two types of ucertainties: stochastic noise in the measurements and bounded errors affecting the system dynamics.These assumptions respond to situations fre-quently 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.

Categories

control theory.

Author keywords

state estimation, interval analysis, box particle filtering, fault detection

Scientific reference

J. Blesa, F. Le Gall, C. Jauberthie and L. Trave-Massuyes. State estimation and fault detection using box particle filtering with stochastic measurements, 26th International Workshop on Principles of Diagnosis, 2015, Paris, pp. 67-73.