Multitarget tracking using sensors with known correlations
© 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., the...
| Main Author: | |
|---|---|
| Format: | Conference Paper |
| Published: |
2016
|
| Online Access: | http://hdl.handle.net/20.500.11937/56380 |
| _version_ | 1848759860592640000 |
|---|---|
| author | Mahler, Ronald |
| author_facet | Mahler, Ronald |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:06:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56380 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:36Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-563802017-09-13T16:11:25Z Multitarget tracking using sensors with known correlations Mahler, Ronald © 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. 2016 Conference Paper http://hdl.handle.net/20.500.11937/56380 10.1117/12.2224112 restricted |
| spellingShingle | Mahler, Ronald Multitarget tracking using sensors with known correlations |
| title | Multitarget tracking using sensors with known correlations |
| title_full | Multitarget tracking using sensors with known correlations |
| title_fullStr | Multitarget tracking using sensors with known correlations |
| title_full_unstemmed | Multitarget tracking using sensors with known correlations |
| title_short | Multitarget tracking using sensors with known correlations |
| title_sort | multitarget tracking using sensors with known correlations |
| url | http://hdl.handle.net/20.500.11937/56380 |