RTS Smoother for GLMB filter
In this paper, we implement a low-cost but effective smoothing strategy to smooth estimated tracks returned by the GLMB filter. While the forward filtering step is carried out via the GLMB filtering procedure, the backward smoothing step is recursively implemented from the final time step to the fir...
| Main Authors: | , |
|---|---|
| Format: | Conference Paper |
| Published: |
2019
|
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/93021 |
| _version_ | 1848765687268376576 |
|---|---|
| author | Nguyen, Tran Thien Dat Yu, J. |
| author_facet | Nguyen, Tran Thien Dat Yu, J. |
| author_sort | Nguyen, Tran Thien Dat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we implement a low-cost but effective smoothing strategy to smooth estimated tracks returned by the GLMB filter. While the forward filtering step is carried out via the GLMB filtering procedure, the backward smoothing step is recursively implemented from the final time step to the first time step via a smoothing algorithm. In particular, the smoothing algorithm is based on the Rauch-Tung-Striebel (RTS) of fixed-interval smoother. We demonstrate our smoothing strategy on a linear Gaussian model and the experimental results show consistent improved tracking performance over 100 Monte Carlo runs. |
| first_indexed | 2025-11-14T11:39:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-93021 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:39:12Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-930212023-11-07T03:05:02Z RTS Smoother for GLMB filter Nguyen, Tran Thien Dat Yu, J. In this paper, we implement a low-cost but effective smoothing strategy to smooth estimated tracks returned by the GLMB filter. While the forward filtering step is carried out via the GLMB filtering procedure, the backward smoothing step is recursively implemented from the final time step to the first time step via a smoothing algorithm. In particular, the smoothing algorithm is based on the Rauch-Tung-Striebel (RTS) of fixed-interval smoother. We demonstrate our smoothing strategy on a linear Gaussian model and the experimental results show consistent improved tracking performance over 100 Monte Carlo runs. 2019 Conference Paper http://hdl.handle.net/20.500.11937/93021 10.1109/ICCAIS46528.2019.9074579 http://purl.org/au-research/grants/arc/DP160104662 fulltext |
| spellingShingle | Nguyen, Tran Thien Dat Yu, J. RTS Smoother for GLMB filter |
| title | RTS Smoother for GLMB filter |
| title_full | RTS Smoother for GLMB filter |
| title_fullStr | RTS Smoother for GLMB filter |
| title_full_unstemmed | RTS Smoother for GLMB filter |
| title_short | RTS Smoother for GLMB filter |
| title_sort | rts smoother for glmb filter |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/93021 |