Distributed fusion of multitarget densities and consensus PHD/CPHD filters
© 2015 SPIE. The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/55333 |
| _version_ | 1848759593574858752 |
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| author | Battistelli, G. Chisci, L. Fantacci, C. Farina, A. Mahler, Ronald |
| author_facet | Battistelli, G. Chisci, L. Fantacci, C. Farina, A. Mahler, Ronald |
| author_sort | Battistelli, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 SPIE. The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network. |
| first_indexed | 2025-11-14T10:02:21Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55333 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:02:21Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-553332017-09-13T16:11:12Z Distributed fusion of multitarget densities and consensus PHD/CPHD filters Battistelli, G. Chisci, L. Fantacci, C. Farina, A. Mahler, Ronald © 2015 SPIE. The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network. 2015 Conference Paper http://hdl.handle.net/20.500.11937/55333 10.1117/12.2176948 restricted |
| spellingShingle | Battistelli, G. Chisci, L. Fantacci, C. Farina, A. Mahler, Ronald Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title | Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title_full | Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title_fullStr | Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title_full_unstemmed | Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title_short | Distributed fusion of multitarget densities and consensus PHD/CPHD filters |
| title_sort | distributed fusion of multitarget densities and consensus phd/cphd filters |
| url | http://hdl.handle.net/20.500.11937/55333 |