Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/90802 |
| _version_ | 1848765432378425344 |
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| author | Vo, Ba-Ngu Vo, Ba Tuong Beard, Michael |
| author_facet | Vo, Ba-Ngu Vo, Ba Tuong Beard, Michael |
| author_sort | Vo, Ba-Ngu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the multi-sensor case require solving multi-dimensional ranked assignment problems, which are NP-hard. The proposed implementation exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a quadratic complexity in the number of hypothesized objects and linear in the total number of measurements from all sensors. |
| first_indexed | 2025-11-14T11:35:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-90802 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:09Z |
| publishDate | 2019 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-908022023-04-20T05:22:38Z Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter Vo, Ba-Ngu Vo, Ba Tuong Beard, Michael Science & Technology Technology Engineering, Electrical & Electronic Engineering State estimation Filtering Random finite sets Multi-dimensional assignment Gibbs sampling RANDOM FINITE SETS DISTRIBUTED FUSION MONTE-CARLO LOCALIZATION This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the multi-sensor case require solving multi-dimensional ranked assignment problems, which are NP-hard. The proposed implementation exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a quadratic complexity in the number of hypothesized objects and linear in the total number of measurements from all sensors. 2019 Journal Article http://hdl.handle.net/20.500.11937/90802 10.1109/TSP.2019.2946023 English http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP160104662 http://creativecommons.org/licenses/by/4.0/ IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering State estimation Filtering Random finite sets Multi-dimensional assignment Gibbs sampling RANDOM FINITE SETS DISTRIBUTED FUSION MONTE-CARLO LOCALIZATION Vo, Ba-Ngu Vo, Ba Tuong Beard, Michael Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title | Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title_full | Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title_fullStr | Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title_full_unstemmed | Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title_short | Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter |
| title_sort | multi-sensor multi-object tracking with the generalized labeled multi-bernoulli filter |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering State estimation Filtering Random finite sets Multi-dimensional assignment Gibbs sampling RANDOM FINITE SETS DISTRIBUTED FUSION MONTE-CARLO LOCALIZATION |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/90802 |