Consensus CPHD filter for distributed multitarget tracking
The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPH...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronic Engineers
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/7924 |
| _version_ | 1848745509267701760 |
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| author | Battistelli, G. Chisci, L. Fantacci, Claudio Farina, A. Graziano, A. |
| author_facet | Battistelli, G. Chisci, L. Fantacci, Claudio Farina, A. Graziano, A. |
| author_sort | Battistelli, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios. |
| first_indexed | 2025-11-14T06:18:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-7924 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:18:29Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronic Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-79242017-09-13T14:37:52Z Consensus CPHD filter for distributed multitarget tracking Battistelli, G. Chisci, L. Fantacci, Claudio Farina, A. Graziano, A. The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios. 2013 Journal Article http://hdl.handle.net/20.500.11937/7924 10.1109/JSTSP.2013.2250911 Institute of Electrical and Electronic Engineers restricted |
| spellingShingle | Battistelli, G. Chisci, L. Fantacci, Claudio Farina, A. Graziano, A. Consensus CPHD filter for distributed multitarget tracking |
| title | Consensus CPHD filter for distributed multitarget tracking |
| title_full | Consensus CPHD filter for distributed multitarget tracking |
| title_fullStr | Consensus CPHD filter for distributed multitarget tracking |
| title_full_unstemmed | Consensus CPHD filter for distributed multitarget tracking |
| title_short | Consensus CPHD filter for distributed multitarget tracking |
| title_sort | consensus cphd filter for distributed multitarget tracking |
| url | http://hdl.handle.net/20.500.11937/7924 |