The Unscented Particle Filter
Abstract
In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented fil(cid:173) ters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms stan(cid:173) dard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.
Cite
Text
van der Merwe et al. "The Unscented Particle Filter." Neural Information Processing Systems, 2000.Markdown
[van der Merwe et al. "The Unscented Particle Filter." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/vandermerwe2000neurips-unscented/)BibTeX
@inproceedings{vandermerwe2000neurips-unscented,
title = {{The Unscented Particle Filter}},
author = {van der Merwe, Rudolph and Doucet, Arnaud and de Freitas, Nando and Wan, Eric A.},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {584-590},
url = {https://mlanthology.org/neurips/2000/vandermerwe2000neurips-unscented/}
}