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dc.contributor.authorNguyen, Tran Thien Dat
dc.contributor.authorYu, J.
dc.date.accessioned2023-08-21T07:41:13Z
dc.date.available2023-08-21T07:41:13Z
dc.date.issued2019
dc.identifier.citationNguyen, T.T.D. and Yu, J. 2019. RTS Smoother for GLMB filter. In 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, China.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/93021
dc.identifier.doi10.1109/ICCAIS46528.2019.9074579
dc.description.abstract

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.

dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP160104662
dc.titleRTS Smoother for GLMB filter
dc.typeConference Paper
dcterms.source.titleICCAIS 2019 - 8th International Conference on Control, Automation and Information Sciences
dcterms.source.isbn9781728123110
dc.date.updated2023-08-21T07:41:12Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidNguyen, Tran Thien Dat [0000-0001-9185-4009]
curtin.repositoryagreementV3


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