Labeled Random Finite Sets and Multi-Object Conjugate Priors
The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations, and noise. This estimation problem can be formulated in a Bayesian fram...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/24008 |
| _version_ | 1848751309838090240 |
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| author | Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations, and noise. This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) that covers thinning, Markov shifts, and superposition. A prior for the hidden RFS together with the likelihood of the realization of the observed RFS gives the posterior distribution via the application of Bayes rule. We propose a new class of RFS distributions that is conjugate with respect to the multiobject observation likelihood and closed under the Chapman-Kolmogorov equation. This result is tested on a Bayesian multi-target tracking algorithm. |
| first_indexed | 2025-11-14T07:50:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-24008 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:50:41Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-240082017-09-13T13:55:44Z Labeled Random Finite Sets and Multi-Object Conjugate Priors Vo, Ba Tuong Vo, Ba-Ngu The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations, and noise. This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) that covers thinning, Markov shifts, and superposition. A prior for the hidden RFS together with the likelihood of the realization of the observed RFS gives the posterior distribution via the application of Bayes rule. We propose a new class of RFS distributions that is conjugate with respect to the multiobject observation likelihood and closed under the Chapman-Kolmogorov equation. This result is tested on a Bayesian multi-target tracking algorithm. 2013 Journal Article http://hdl.handle.net/20.500.11937/24008 10.1109/TSP.2013.2259822 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | Vo, Ba Tuong Vo, Ba-Ngu Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title | Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title_full | Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title_fullStr | Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title_full_unstemmed | Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title_short | Labeled Random Finite Sets and Multi-Object Conjugate Priors |
| title_sort | labeled random finite sets and multi-object conjugate priors |
| url | http://hdl.handle.net/20.500.11937/24008 |