Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
Computation
2017-11-30 v1 Systems and Control
Abstract
When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likelihood function and its derivatives are inherently noisy. The key idea in this paper is to run a particle filter based on a current parameter estimate, but then use the output from this particle filter to re-evaluate the likelihood function approximation also for other parameter values. This results in a (local) deterministic approximation of the likelihood and any standard optimization routine can be applied to find the maximum of this local approximation. By iterating this procedure we eventually arrive at a final parameter estimate.
Cite
@article{arxiv.1711.10765,
title = {Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations},
author = {Andreas Svensson and Fredrik Lindsten and Thomas B. Schön},
journal= {arXiv preprint arXiv:1711.10765},
year = {2017}
}