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.
CitationBlesa, 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.
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