A Direct Sampling Particle Filter from Approximate Conditional Density Function Supported on Constrained State Space
Computers & Chemical Engineering
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 space. A direct sampling algorithm is proposed for handling linear and nonlinear equality and inequality constraints. The algorithm uses the constrained mode for nonlinear constraints.
Ungarala, Sridhar, "A Direct Sampling Particle Filter from Approximate Conditional Density Function Supported on Constrained State Space" (2011). Chemical & Biomedical Engineering Faculty Publications. 73.
Ungarala, S. (2011). A direct sampling particle filter from approximate conditional density function supported on constrained state space. Computers and Chemical Engineering, 35(6), 1110-1118. doi:10.1016/j.compchemeng.2010.07.022
NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Chemical Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Chemical Engineering, 35, 6, (June 9, 2011) DOI 10.1016/j.compchemeng.2010.07.022