Year: 2015
Type: preprint
Abstract: In this paper, we propose a meshfree approximation method for the implicit filter developed in [2], which is a novel numerical algorithm for nonlinear filtering problems. The implicit filter approximates conditional distributions in the optimal filter over a deterministic state space grid and is developed from samples of the current state obtained by solving the state equation implicitly. The purpose of the meshfree approximation is to improve the efficiency of the implicit filter in moderately high-dimensional problems. The construction of the algorithm includes generation of random state space points and a meshfree interpolation method. Numerical experiments show the effectiveness and efficiency of our algorithm.
Source: arXiv (Cornell University)
Institutions: Auburn University, Oak Ridge National Laboratory
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1026679911, G121915436, EQUINOX: ENVIRONMENT FOR QUANTIFYING UNCERTAINTY: INTEGRATED AND OPTIMIZED AT THE EXTREME SCALE, G1645119126, G166562317, G203625172, G236881256, G2757267231, G2988778103, G313676650, G3704101847, G3841132129, G4423657506, EQUINOX: ENVIRONMENT FOR QUANTIFYING UNCERTAINTY: INTEGRATED AND OPTIMIZED AT THE EXTREME SCALE, G5726405315, G67284079, G675320460, G7090116110, G8269158468, G8906985441, G8943143067
OpenAlex ID: W1921083195