García-Fernández, AF, Morelande, MR and Grajal, J
(2012)
Mixture truncated unscented Kalman filtering.
.
|
Text
PID2352191.pdf - Author Accepted Manuscript Download (545kB) | Preview |
Abstract
This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi).
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 19 Apr 2021 09:21 |
| Last Modified: | 18 Jan 2023 22:52 |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3119491 |
Altmetric
Altmetric
