Multitarget tracking using sensors with known correlations
dc.contributor.author | Mahler, Ronald | |
dc.date.accessioned | 2017-08-24T02:23:42Z | |
dc.date.available | 2017-08-24T02:23:42Z | |
dc.date.created | 2017-08-23T07:21:50Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Mahler, R. 2016. Multitarget tracking using sensors with known correlations. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/56380 | |
dc.identifier.doi | 10.1117/12.2224112 | |
dc.description.abstract |
© 2016 SPIE. This paper is the fourth in a series aimed at weakening the independence assumptions that are typically presumed in multitarget tracking. Specifically, we assume that, in a multisensory scenario, the sensors are not necessarily independent but, rather, have known correlations (i.e., their joint single-target joint likelihood function is known). From this, we construct a multitarget measurement model for sensors with known correlations. From this model we derive, as an illustrative example, the filtering equations for a probability hypothesis density (PHD) filter for sensors with known correlations. We emphasize the two-sensor case of this filter, for which the measurement-update equations involve a summation over all measurement-to-measurement associations between the two sensors. | |
dc.title | Multitarget tracking using sensors with known correlations | |
dc.type | Conference Paper | |
dcterms.source.volume | 9842 | |
dcterms.source.title | Proceedings of SPIE - The International Society for Optical Engineering | |
dcterms.source.series | Proceedings of SPIE - The International Society for Optical Engineering | |
dcterms.source.isbn | 9781510600836 | |
curtin.department | Department of Electrical and Computer Engineering | |
curtin.accessStatus | Fulltext not available |
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