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مجله کنترل فرآیند
جزئیات رویداد
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  • on the choice of importance distributions for unconstrained and constrained state estimation using particle filter

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    • تاریخ ارائه: 1392/01/01
    • تاریخ انتشار در تی پی بین: 1392/01/01
    • تعداد بازدید: 622
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     recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. for nonlinear/non-gaussian state estimation problems, particle filters have been widely used (arulampalam et al). as pointed out by daum, particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. in this paper, a novel approach for generating the proposal distribution based on a constrained extended kalman filter (c-ekf), constrained unscented kalman filter (c-ukf) and constrained ensemble kalman filter (c-enkf) has been proposed. the efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (rao et al). we also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with unscented kalman filter as a proposal and unconstrained ensemble kalman filter (enkf) as a proposal. the efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. the simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters.

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