Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually...
| Main Authors: | , , , , |
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| Format: | Journal Article |
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Institute of Electrical and Electronics Engineers
2015
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| Online Access: | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/24520 |
| _version_ | 1848751453561159680 |
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| author | Papi, Francesco Ba-Ngu, V. Ba-Tuong, V. Fantacci, C. Beard, M. |
| author_facet | Papi, Francesco Ba-Ngu, V. Ba-Tuong, V. Fantacci, C. Beard, M. |
| author_sort | Papi, Francesco |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach. |
| first_indexed | 2025-11-14T07:52:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-24520 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:52:58Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-245202022-10-12T02:36:51Z Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities Papi, Francesco Ba-Ngu, V. Ba-Tuong, V. Fantacci, C. Beard, M. In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach. 2015 Journal Article http://hdl.handle.net/20.500.11937/24520 10.1109/TSP.2015.2454478 http://purl.org/au-research/grants/arc/DP130104404 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | Papi, Francesco Ba-Ngu, V. Ba-Tuong, V. Fantacci, C. Beard, M. Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title | Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title_full | Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title_fullStr | Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title_full_unstemmed | Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title_short | Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities |
| title_sort | generalized labeled multi-bernoulli approximation of multi-object densities |
| url | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/24520 |