Generalised labelled multi-Bernoulli forward-backward smoothing
This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB f...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://ieeexplore.ieee.org/abstract/document/7527954/ http://hdl.handle.net/20.500.11937/50665 |
| _version_ | 1848758515691159552 |
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| author | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Beard, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections. |
| first_indexed | 2025-11-14T09:45:13Z |
| format | Conference Paper |
| id | curtin-20.500.11937-50665 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:45:13Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-506652018-12-14T01:01:20Z Generalised labelled multi-Bernoulli forward-backward smoothing Beard, M. Vo, Ba Tuong Vo, Ba-Ngu This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing recursions, which involves a forward pass using the previously developed GLMB filter, followed by backward propagation of a corrector that is used to obtain the smoothed GLMB density. The smoother is derived under the assumptions of the standard multi-object dynamic model, and the standard multi-object measurement likelihood model, i.e. The proposed smoother is capable of handling an unknown and time-varying number of objects, in the presence of measurement origin uncertainty, clutter, and missed detections. 2016 Conference Paper http://hdl.handle.net/20.500.11937/50665 http://ieeexplore.ieee.org/abstract/document/7527954/ restricted |
| spellingShingle | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu Generalised labelled multi-Bernoulli forward-backward smoothing |
| title | Generalised labelled multi-Bernoulli forward-backward smoothing |
| title_full | Generalised labelled multi-Bernoulli forward-backward smoothing |
| title_fullStr | Generalised labelled multi-Bernoulli forward-backward smoothing |
| title_full_unstemmed | Generalised labelled multi-Bernoulli forward-backward smoothing |
| title_short | Generalised labelled multi-Bernoulli forward-backward smoothing |
| title_sort | generalised labelled multi-bernoulli forward-backward smoothing |
| url | http://ieeexplore.ieee.org/abstract/document/7527954/ http://hdl.handle.net/20.500.11937/50665 |