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Full-Text Articles in Engineering
A Direct Sampling Particle Filter From Approximate Conditional Density Function Supported On Constrained State Space, Sridhar Ungarala
A Direct Sampling Particle Filter From Approximate Conditional Density Function Supported On Constrained State Space, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Constraints on the state vector must be taken into account in the state estimation problem. Recently, acceptance/rejection and projection methods are proposed in the particle filter framework for constraining the particles. A weighted least squares formulation is used for constraining samples in unscented and ensemble Kalman filters. In this paper, direct sampling from an approximate conditional probability density function (pdf) is proposed. It is obtained by approximating the a priori pdf as a Gaussian. The support of the conditional density is a subset of the intersection of two supports, the 3-sigma bounds of the priori Gaussian and the constrained state …
Computing Arrival Cost Parameters In Moving Horizon Estimation Using Sampling Based Filters, Sridhar Ungarala
Computing Arrival Cost Parameters In Moving Horizon Estimation Using Sampling Based Filters, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Moving horizon estimation (MHE) is a numerical optimization based approach to state estimation, where the joint probability density function (pdf) of a finite state trajectory is sought, which is conditioned on a moving horizon of measurements. The joint conditional pdf depends on the a priori state pdf at the start of the horizon, which is a prediction pdf based on historical data outside the horizon. When the joint pdf is maximized, the arrival cost is a penalty term based on the a priori pdf in the MHE objective function. Traditionally, the a priori pdf is assumed as …
Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems, Lixin Lang, Wen-Shiang Chen, Bhavik R. Bakshi, Prem K. Goel, Sridhar Ungarala
Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems, Lixin Lang, Wen-Shiang Chen, Bhavik R. Bakshi, Prem K. Goel, Sridhar Ungarala
Chemical & Biomedical Engineering Faculty Publications
Nonlinear and non-Gaussian processes with constraints are commonly encountered in dynamic estimation problems. Methods for solving such problems either ignore the constraints or rely on crude approximations of the model or probability distributions. Such approximations may reduce the accuracy of the estimates since they often fail to capture the variety of probability distributions encountered in constrained linear and nonlinear dynamic systems. This article describes a practical approach that overcomes these shortcomings via a novel extension of sequential Monte Carlo (SMC) sampling or particle filtering. Inequality constraints are imposed by accept/reject steps in the algorithm. The proposed approach provides samples representing …