An overview of particle methods for random finite set models
This overview paper describes the particle methods developed for the implementation of the class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
Elsevier
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/52414 |
| _version_ | 1848758920545304576 |
|---|---|
| author | Ristic, B. Beard, Michael Fantacci, C. |
| author_facet | Ristic, B. Beard, Michael Fantacci, C. |
| author_sort | Ristic, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This overview paper describes the particle methods developed for the implementation of the class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application. |
| first_indexed | 2025-11-14T09:51:39Z |
| format | Journal Article |
| id | curtin-20.500.11937-52414 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:51:39Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-524142018-03-29T09:09:01Z An overview of particle methods for random finite set models Ristic, B. Beard, Michael Fantacci, C. This overview paper describes the particle methods developed for the implementation of the class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application. 2016 Journal Article http://hdl.handle.net/20.500.11937/52414 10.1016/j.inffus.2016.02.004 Elsevier restricted |
| spellingShingle | Ristic, B. Beard, Michael Fantacci, C. An overview of particle methods for random finite set models |
| title | An overview of particle methods for random finite set models |
| title_full | An overview of particle methods for random finite set models |
| title_fullStr | An overview of particle methods for random finite set models |
| title_full_unstemmed | An overview of particle methods for random finite set models |
| title_short | An overview of particle methods for random finite set models |
| title_sort | overview of particle methods for random finite set models |
| url | http://hdl.handle.net/20.500.11937/52414 |