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
Authors
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Blesa Izquierdo, Joaquim
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Le Gall, Françoise
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Jauberthie, Carine
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Trave-Massuyes, Louise
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.
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