Computationally-tractable approximate PHD and CPHD filters for superpositional sensors
In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter ap...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institute of Electrical and Electronic Engineers
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/55359 |
| _version_ | 1848759600810033152 |
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| author | Nannuru, S. Coates, M. Mahler, Ronald |
| author_facet | Nannuru, S. Coates, M. Mahler, Ronald |
| author_sort | Nannuru, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. © 2007-2012 IEEE. |
| first_indexed | 2025-11-14T10:02:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-55359 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:02:28Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronic Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-553592017-09-13T16:10:07Z Computationally-tractable approximate PHD and CPHD filters for superpositional sensors Nannuru, S. Coates, M. Mahler, Ronald In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations. As an example, we present simulation experiments that involve tracking multiple targets using acoustic amplitude sensors and a radio-frequency tomography sensor system. Our simulation study indicates that the CPHD filter provides promising tracking accuracy with reasonable computational requirements. © 2007-2012 IEEE. 2013 Journal Article http://hdl.handle.net/20.500.11937/55359 10.1109/JSTSP.2013.2251605 Institute of Electrical and Electronic Engineers restricted |
| spellingShingle | Nannuru, S. Coates, M. Mahler, Ronald Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title | Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title_full | Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title_fullStr | Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title_full_unstemmed | Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title_short | Computationally-tractable approximate PHD and CPHD filters for superpositional sensors |
| title_sort | computationally-tractable approximate phd and cphd filters for superpositional sensors |
| url | http://hdl.handle.net/20.500.11937/55359 |