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Rao-Blackwellized Auxiliary Particle Filters for Mixed Linear/Nonlinear Gaussian models

Nordh, Jerker LU (2014) 12th International Conference on Signal Processing In [Host publication title missing] p.1-6
Abstract
The Auxiliary Particle Filter is a variant of the common particle filter which attempts to incorporate information from the next measurement to improve the proposal distribution in the update step. This paper studies how this can be done for Mixed Linear/Nonlinear Gaussian models, it builds on a previously suggested method and introduces two new variants which tries to improve the performance by using a more detailed approximation of the true probability density function when evaluating the so called first stage weights. These algorithms are compared for a couple of models to illustrate their strengths and

weaknesses.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
[Host publication title missing]
pages
1 - 6
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
12th International Conference on Signal Processing
external identifiers
  • wos:000365529500001
  • scopus:84937913483
ISSN
2164-5221
language
English
LU publication?
yes
id
32123adf-1ef0-418f-a9bc-eba17018848c (old id 4739270)
date added to LUP
2014-11-09 18:29:45
date last changed
2017-01-01 06:20:31
@inproceedings{32123adf-1ef0-418f-a9bc-eba17018848c,
  abstract     = {The Auxiliary Particle Filter is a variant of the common particle filter which attempts to incorporate information from the next measurement to improve the proposal distribution in the update step. This paper studies how this can be done for Mixed Linear/Nonlinear Gaussian models, it builds on a previously suggested method and introduces two new variants which tries to improve the performance by using a more detailed approximation of the true probability density function when evaluating the so called first stage weights. These algorithms are compared for a couple of models to illustrate their strengths and<br/><br>
weaknesses.},
  author       = {Nordh, Jerker},
  booktitle    = {[Host publication title missing]},
  issn         = {2164-5221},
  language     = {eng},
  pages        = {1--6},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Rao-Blackwellized Auxiliary Particle Filters for Mixed Linear/Nonlinear Gaussian models},
  year         = {2014},
}