Forward-Backward Probability Hypothesis Density Smoothing
A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Aerospace & Electronic Systems Society
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/47396 |
| _version_ | 1848757821369221120 |
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| author | Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Mahler, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the proposed backward PHD recursion is exact and does not require the previous iterate to be Poisson. In addition, assuming the previous iterate is Poisson, the cardinality distribution and all moments of the backward-smoothed multi-target density are derived. It is also shown that PHD smoothing alone does not necessarily improve cardinality estimation. Using an appropriate particle implementation we present a number of experiments to investigate the ability of the proposed multi-target smoother to correct state as well as cardinality errors. |
| first_indexed | 2025-11-14T09:34:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-47396 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:34:11Z |
| publishDate | 2012 |
| publisher | Aerospace & Electronic Systems Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-473962017-09-13T14:11:36Z Forward-Backward Probability Hypothesis Density Smoothing Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using finite set statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the proposed backward PHD recursion is exact and does not require the previous iterate to be Poisson. In addition, assuming the previous iterate is Poisson, the cardinality distribution and all moments of the backward-smoothed multi-target density are derived. It is also shown that PHD smoothing alone does not necessarily improve cardinality estimation. Using an appropriate particle implementation we present a number of experiments to investigate the ability of the proposed multi-target smoother to correct state as well as cardinality errors. 2012 Journal Article http://hdl.handle.net/20.500.11937/47396 10.1109/TAES.2012.6129665 Aerospace & Electronic Systems Society restricted |
| spellingShingle | Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu Forward-Backward Probability Hypothesis Density Smoothing |
| title | Forward-Backward Probability Hypothesis Density Smoothing |
| title_full | Forward-Backward Probability Hypothesis Density Smoothing |
| title_fullStr | Forward-Backward Probability Hypothesis Density Smoothing |
| title_full_unstemmed | Forward-Backward Probability Hypothesis Density Smoothing |
| title_short | Forward-Backward Probability Hypothesis Density Smoothing |
| title_sort | forward-backward probability hypothesis density smoothing |
| url | http://hdl.handle.net/20.500.11937/47396 |