Robust multi-sensor generalized labeled multi-Bernoulli filter
This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estim...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
ELSEVIER
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/96501 |
| _version_ | 1848766159619358720 |
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| author | Do, Cong-Thanh Nguyen, Tran Thien Dat Nguyen, Hoa Van |
| author_facet | Do, Cong-Thanh Nguyen, Tran Thien Dat Nguyen, Hoa Van |
| author_sort | Do, Cong-Thanh |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target trajectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Experimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probability. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly. |
| first_indexed | 2025-11-14T11:46:43Z |
| format | Journal Article |
| id | curtin-20.500.11937-96501 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:46:43Z |
| publishDate | 2022 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-965012025-01-10T03:40:00Z Robust multi-sensor generalized labeled multi-Bernoulli filter Do, Cong-Thanh Nguyen, Tran Thien Dat Nguyen, Hoa Van Science & Technology Technology Engineering, Electrical & Electronic Engineering Multi-sensor GLMB filter Robust tracking Bearing-only sensors Bootstrapping method Labeled random finite sets RANDOM FINITE SETS TRACKING FUSION eess.SP eess.SP This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target trajectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Experimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probability. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly. 2022 Journal Article http://hdl.handle.net/20.500.11937/96501 10.1016/j.sigpro.2021.108368 English http://creativecommons.org/licenses/by-nc-nd/4.0/ ELSEVIER fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering Multi-sensor GLMB filter Robust tracking Bearing-only sensors Bootstrapping method Labeled random finite sets RANDOM FINITE SETS TRACKING FUSION eess.SP eess.SP Do, Cong-Thanh Nguyen, Tran Thien Dat Nguyen, Hoa Van Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title | Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title_full | Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title_fullStr | Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title_full_unstemmed | Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title_short | Robust multi-sensor generalized labeled multi-Bernoulli filter |
| title_sort | robust multi-sensor generalized labeled multi-bernoulli filter |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering Multi-sensor GLMB filter Robust tracking Bearing-only sensors Bootstrapping method Labeled random finite sets RANDOM FINITE SETS TRACKING FUSION eess.SP eess.SP |
| url | http://hdl.handle.net/20.500.11937/96501 |