Tracking correlated, simultaneously evolving target populations
© 2016 SPIE. Multisensor-multitarget tracking algorithms are typically based on numerous statistical independence assumptions. This paper is the fifth in a series aimed at weakening such assumptions. It addresses the statistics of correlated, simultaneously evolving multitarget populations. The corr...
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| Format: | Conference Paper |
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2016
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| Online Access: | http://hdl.handle.net/20.500.11937/56139 |
| _version_ | 1848759796210073600 |
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| author | Mahler, Ronald |
| author_facet | Mahler, Ronald |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2016 SPIE. Multisensor-multitarget tracking algorithms are typically based on numerous statistical independence assumptions. This paper is the fifth in a series aimed at weakening such assumptions. It addresses the statistics of correlated, simultaneously evolving multitarget populations. The correlation between two multitarget popula-tions is approximately modeled using bivariate i.i.d.c. (independent, identically distributed cluster) distributions. Based on this, a joint tracking filter for such populations is devised, in analogy with the cardinalized probability hypothesis density (CPHD) filter. |
| first_indexed | 2025-11-14T10:05:34Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56139 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:34Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-561392017-09-13T16:10:28Z Tracking correlated, simultaneously evolving target populations Mahler, Ronald © 2016 SPIE. Multisensor-multitarget tracking algorithms are typically based on numerous statistical independence assumptions. This paper is the fifth in a series aimed at weakening such assumptions. It addresses the statistics of correlated, simultaneously evolving multitarget populations. The correlation between two multitarget popula-tions is approximately modeled using bivariate i.i.d.c. (independent, identically distributed cluster) distributions. Based on this, a joint tracking filter for such populations is devised, in analogy with the cardinalized probability hypothesis density (CPHD) filter. 2016 Conference Paper http://hdl.handle.net/20.500.11937/56139 10.1117/12.2224640 restricted |
| spellingShingle | Mahler, Ronald Tracking correlated, simultaneously evolving target populations |
| title | Tracking correlated, simultaneously evolving target populations |
| title_full | Tracking correlated, simultaneously evolving target populations |
| title_fullStr | Tracking correlated, simultaneously evolving target populations |
| title_full_unstemmed | Tracking correlated, simultaneously evolving target populations |
| title_short | Tracking correlated, simultaneously evolving target populations |
| title_sort | tracking correlated, simultaneously evolving target populations |
| url | http://hdl.handle.net/20.500.11937/56139 |