An approximate CPHD filter for superpositional sensors
Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most f...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/56296 |
| _version_ | 1848759837607854080 |
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| author | Mahler, Ronald El-Fallah, A. |
| author_facet | Mahler, Ronald El-Fallah, A. |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). |
| first_indexed | 2025-11-14T10:06:14Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56296 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:06:14Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-562962017-09-13T16:10:29Z An approximate CPHD filter for superpositional sensors Mahler, Ronald El-Fallah, A. Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). 2012 Conference Paper http://hdl.handle.net/20.500.11937/56296 10.1117/12.975965 restricted |
| spellingShingle | Mahler, Ronald El-Fallah, A. An approximate CPHD filter for superpositional sensors |
| title | An approximate CPHD filter for superpositional sensors |
| title_full | An approximate CPHD filter for superpositional sensors |
| title_fullStr | An approximate CPHD filter for superpositional sensors |
| title_full_unstemmed | An approximate CPHD filter for superpositional sensors |
| title_short | An approximate CPHD filter for superpositional sensors |
| title_sort | approximate cphd filter for superpositional sensors |
| url | http://hdl.handle.net/20.500.11937/56296 |