Rao-Blackwellized Auxiliary Particle Filters for Mixed Linear/Nonlinear Gaussian models
(2014) 12th International Conference on Signal Processing 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:
https://lup.lub.lu.se/record/4739270
- author
- Nordh, Jerker LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- pages
- 1 - 6
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 12th International Conference on Signal Processing
- conference location
- Hangzhou, China
- conference dates
- 2014-10-20
- 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
- 2016-04-01 14:37:39
- date last changed
- 2024-06-04 15:07:14
@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}}, url = {{https://lup.lub.lu.se/search/files/4073391/4739271.pdf}}, year = {{2014}}, }