Open AccessRegularized Particle Filter with Langevin Resampling Step
Author(s) -
Lian Duan,
Chris L. Farmer,
Irene M. Moroz,
Theodore E. Simos,
George Psihoyios,
Ch. Tsitouras
Publication year - 2010
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.3497827
Subject(s) - ensemble kalman filter , particle filter , mathematics , gaussian , filtering problem , filter (signal processing) , auxiliary particle filter , nonlinear filter , resampling , gaussian noise , algorithm , mathematical optimization , kalman filter , filter design , computer science , extended kalman filter , physics , statistics , quantum mechanics , computer vision
The solution of an inverse problem involves the estimation of variables and parameters values given by the state-space system. While a general (infinite-dimensional) optimal filter theory [1, 2] exists for nonlinear systems with Gaussian or non-Gaussian noise, applications rely on (finite-dimensional) suboptimal approximations to the optimal filter for practical implementations. The most widely-studied filters of this kind include the Regularized Particle Filter (RPF) [3, 4] and the Ensemble Square Root Filter (EnSRF) [5]. The latter is an ad-hoc approximation to the Bayes Filter, while the former is rigorously formulated, based upon the Glivenko-Cantelli theorem. By introducing a new global resampling step to the RPF, the EnSRF is proved to approximate the RPF in a special case
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