Bayesian Filtering With Random Finite Set Observations
This paper presents a novel and mathematically rigorous Bayes’ recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a consistent likelihood function derive...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/14299 |
| _version_ | 1848748586500620288 |
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| author | Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio |
| author_facet | Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel and mathematically rigorous Bayes’ recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes’ recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptions, an exact closed-form solution to the proposed recursion is derived, and efficient implementations are given. Extensions of the closed-form recursion to accommodate mild nonlinearities are also given using linearization and unscented transforms. |
| first_indexed | 2025-11-14T07:07:24Z |
| format | Journal Article |
| id | curtin-20.500.11937-14299 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:07:24Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-142992017-09-13T14:04:53Z Bayesian Filtering With Random Finite Set Observations Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio PHD filter Kalman filter target tracking point processes Gaussian sum filter CPHD filter particle filter Bayesian filtering random finite sets This paper presents a novel and mathematically rigorous Bayes’ recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a consistent likelihood function derived from random finite set theory. It is established that under certain assumptions, the proposed Bayes’ recursion reduces to the cardinalized probability hypothesis density (CPHD) recursion for a single target. A particle implementation of the proposed recursion is given. Under linear Gaussian and constant sensor field of view assumptions, an exact closed-form solution to the proposed recursion is derived, and efficient implementations are given. Extensions of the closed-form recursion to accommodate mild nonlinearities are also given using linearization and unscented transforms. 2008 Journal Article http://hdl.handle.net/20.500.11937/14299 10.1109/TSP.2007.908968 IEEE fulltext |
| spellingShingle | PHD filter Kalman filter target tracking point processes Gaussian sum filter CPHD filter particle filter Bayesian filtering random finite sets Vo, Ba Tuong Vo, Ba-Ngu Cantoni, Antonio Bayesian Filtering With Random Finite Set Observations |
| title | Bayesian Filtering With Random Finite Set Observations |
| title_full | Bayesian Filtering With Random Finite Set Observations |
| title_fullStr | Bayesian Filtering With Random Finite Set Observations |
| title_full_unstemmed | Bayesian Filtering With Random Finite Set Observations |
| title_short | Bayesian Filtering With Random Finite Set Observations |
| title_sort | bayesian filtering with random finite set observations |
| topic | PHD filter Kalman filter target tracking point processes Gaussian sum filter CPHD filter particle filter Bayesian filtering random finite sets |
| url | http://hdl.handle.net/20.500.11937/14299 |