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Communication Dans Un Congrès Année : 2015

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.
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Dates et versions

hal-01206548 , version 1 (30-10-2015)

Identifiants

  • HAL Id : hal-01206548 , version 1

Citer

Joaquim Blesa, Françoise Le Gall, Carine Jauberthie, Louise Travé-Massuyès. State estimation and fault detection using box particle filtering with stochastic measurements. 26th International Workshop on Principles of Diagnosis (DX-15), Aug 2015, Paris, France. pp.67-73. ⟨hal-01206548⟩
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