Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization
In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and impli...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2015
|
| Online Access: | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/24733 |
| _version_ | 1848751511215013888 |
|---|---|
| author | Garcia-Fernandez, Angel Vo, Ba-Ngu |
| author_facet | Garcia-Fernandez, Angel Vo, Ba-Ngu |
| author_sort | Garcia-Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and implicitly perform Kullback-Leibler divergence (KLD) minimizations after the prediction and update steps. We perform the KLD minimizations directly on the multitarget prediction and posterior densities. |
| first_indexed | 2025-11-14T07:53:53Z |
| format | Journal Article |
| id | curtin-20.500.11937-24733 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:53:53Z |
| publishDate | 2015 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-247332022-10-12T02:37:34Z Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization Garcia-Fernandez, Angel Vo, Ba-Ngu In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and implicitly perform Kullback-Leibler divergence (KLD) minimizations after the prediction and update steps. We perform the KLD minimizations directly on the multitarget prediction and posterior densities. 2015 Journal Article http://hdl.handle.net/20.500.11937/24733 10.1109/TSP.2015.2468677 http://purl.org/au-research/grants/arc/DP130104404 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC restricted |
| spellingShingle | Garcia-Fernandez, Angel Vo, Ba-Ngu Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title | Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title_full | Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title_fullStr | Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title_full_unstemmed | Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title_short | Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization |
| title_sort | derivation of the phd and cphd filters based on direct kullback-leibler divergence minimization |
| url | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/24733 |