Sensor control for multi-target tracking using Cauchy-Schwarz divergence
In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteri...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/26907 |
| _version_ | 1848752118068936704 |
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| author | Beard, M. Vo, Ba-Ngu Vo, Ba Tuong Arulampalam, S. |
| author_facet | Beard, M. Vo, Ba-Ngu Vo, Ba Tuong Arulampalam, S. |
| author_sort | Beard, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteriors are generalised labelled multi-Bernoulli (GLMB) densities, which do not permit closed form solutions for traditional information divergence measures such as Kullback-Leibler or Rényi. However, we demonstrate that the Cauchy-Schwarz divergence admits a closed form solution for GLMB densities, thus it can be used as a tractable objective function for multi-target sensor control. This is demonstrated with an application to sensor trajectory optimisation for bearings-only multi-target tracking. |
| first_indexed | 2025-11-14T08:03:32Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26907 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:03:32Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-269072017-01-30T12:55:55Z Sensor control for multi-target tracking using Cauchy-Schwarz divergence Beard, M. Vo, Ba-Ngu Vo, Ba Tuong Arulampalam, S. In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteriors are generalised labelled multi-Bernoulli (GLMB) densities, which do not permit closed form solutions for traditional information divergence measures such as Kullback-Leibler or Rényi. However, we demonstrate that the Cauchy-Schwarz divergence admits a closed form solution for GLMB densities, thus it can be used as a tractable objective function for multi-target sensor control. This is demonstrated with an application to sensor trajectory optimisation for bearings-only multi-target tracking. 2015 Conference Paper http://hdl.handle.net/20.500.11937/26907 restricted |
| spellingShingle | Beard, M. Vo, Ba-Ngu Vo, Ba Tuong Arulampalam, S. Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title | Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title_full | Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title_fullStr | Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title_full_unstemmed | Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title_short | Sensor control for multi-target tracking using Cauchy-Schwarz divergence |
| title_sort | sensor control for multi-target tracking using cauchy-schwarz divergence |
| url | http://hdl.handle.net/20.500.11937/26907 |