Sensor management for multi-target tracking via multi-bernoulli filtering
In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP...
| Main Authors: | , |
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| Format: | Journal Article |
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Pergamon Press
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/21965 |
| _version_ | 1848750738644140032 |
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| author | Hoang, Hung Vo, Ba Tuong |
| author_facet | Hoang, Hung Vo, Ba Tuong |
| author_sort | Hoang, Hung |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability. |
| first_indexed | 2025-11-14T07:41:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-21965 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:41:36Z |
| publishDate | 2014 |
| publisher | Pergamon Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-219652019-02-19T04:26:12Z Sensor management for multi-target tracking via multi-bernoulli filtering Hoang, Hung Vo, Ba Tuong In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability. 2014 Journal Article http://hdl.handle.net/20.500.11937/21965 10.1016/j.automatica.2014.02.007 Pergamon Press fulltext |
| spellingShingle | Hoang, Hung Vo, Ba Tuong Sensor management for multi-target tracking via multi-bernoulli filtering |
| title | Sensor management for multi-target tracking via multi-bernoulli filtering |
| title_full | Sensor management for multi-target tracking via multi-bernoulli filtering |
| title_fullStr | Sensor management for multi-target tracking via multi-bernoulli filtering |
| title_full_unstemmed | Sensor management for multi-target tracking via multi-bernoulli filtering |
| title_short | Sensor management for multi-target tracking via multi-bernoulli filtering |
| title_sort | sensor management for multi-target tracking via multi-bernoulli filtering |
| url | http://hdl.handle.net/20.500.11937/21965 |