The Smooth Trajectory Estimator for LMB Filters
This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB rando...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2023
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| Online Access: | http://purl.org/au-research/grants/arc/LP200301507 http://hdl.handle.net/20.500.11937/96496 |
| _version_ | 1848766158297104384 |
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| author | Nguyen, Hoa Van Nguyen, Tran Thien Dat Shim, Changbeom Anuar, M. |
| author_facet | Nguyen, Hoa Van Nguyen, Tran Thien Dat Shim, Changbeom Anuar, M. |
| author_sort | Nguyen, Hoa Van |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter. |
| first_indexed | 2025-11-14T11:46:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-96496 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:42Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-964962025-01-09T06:39:34Z The Smooth Trajectory Estimator for LMB Filters Nguyen, Hoa Van Nguyen, Tran Thien Dat Shim, Changbeom Anuar, M. This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter. 2023 Conference Paper http://hdl.handle.net/20.500.11937/96496 10.1109/ICCAIS59597.2023.10382267 http://purl.org/au-research/grants/arc/LP200301507 fulltext |
| spellingShingle | Nguyen, Hoa Van Nguyen, Tran Thien Dat Shim, Changbeom Anuar, M. The Smooth Trajectory Estimator for LMB Filters |
| title | The Smooth Trajectory Estimator for LMB Filters |
| title_full | The Smooth Trajectory Estimator for LMB Filters |
| title_fullStr | The Smooth Trajectory Estimator for LMB Filters |
| title_full_unstemmed | The Smooth Trajectory Estimator for LMB Filters |
| title_short | The Smooth Trajectory Estimator for LMB Filters |
| title_sort | smooth trajectory estimator for lmb filters |
| url | http://purl.org/au-research/grants/arc/LP200301507 http://hdl.handle.net/20.500.11937/96496 |