# Science Article Improving the particle filter for high-dimensional problems using artificial process noise

## Abstract

The particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters suffer from degeneracy of the particle weights for high-dimensional problems. We propose a method for improving the performance of the particle filter for certain challenging state space models, with implications for high-dimensional inference. First we approximate the model by adding artificial process noise in an additional state update, then we design a proposal that combines the standard and the locally optimal proposal. This results in a bias-variance trade-off, where adding more noise reduces the variance of the estimate but increases the model bias. The performance of the proposed method is evaluated on a linear Gaussian state space model and on the non-linear Lorenz’96 model. For both models we observe a significant improvement in performance over the standard particle filter.

## Reference

A. Wigren, L.M. Murray, F. Lindsten (2018). Improving the particle filter for high-dimensional problems using artificial process noise. 18th IFAC Symposium on System Identification (SYSID 2018). url:https://arxiv.org/abs/1801.07000.

## BibTeX

@Article{,
title = {Improving the particle filter for high-dimensional problems using artificial process noise},
author = {Anna Wigren and Lawrence M. Murray and Fredrik Lindsten},
journal = {18th IFAC Symposium on System Identification (SYSID 2018)},
year = {2018},
url = {https://arxiv.org/abs/1801.07000}
}