Multi-target tracking with merged measurements using labelled random finite sets
In real world multi-target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, the vast majority of tracking algorithms in the literature assume that each target generates independent measurements. Allowing for the possibility of measurement merging...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2014
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| Online Access: | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6916117 http://hdl.handle.net/20.500.11937/40489 |
| _version_ | 1848755885087653888 |
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| author | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author2 | Juan M. Corchado |
| author_facet | Juan M. Corchado Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Beard, Michael |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In real world multi-target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, the vast majority of tracking algorithms in the literature assume that each target generates independent measurements. Allowing for the possibility of measurement merging increases the computational complexity of the multi-target tracking problem, and limited computing power has been a major factor in the dominance of algorithms that assume independent measurements. In the presence of merged measurements, these algorithms suffer from performance degradation, usually due to premature track termination. In this paper, we develop a principled Bayesian solution to this problem based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalised labelled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects. |
| first_indexed | 2025-11-14T09:03:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40489 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:03:24Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-404892023-02-27T07:34:25Z Multi-target tracking with merged measurements using labelled random finite sets Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Juan M. Corchado James Llinas Jesus Garcia Jose Manuel Molina Javier Bajo Stefano Coraluppi David Hall Moises Sudit Alan Steinberg T. Kirubarajan Eloi Bosse Kellyn Rein Subrata Das Uwe Hanebeck In real world multi-target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, the vast majority of tracking algorithms in the literature assume that each target generates independent measurements. Allowing for the possibility of measurement merging increases the computational complexity of the multi-target tracking problem, and limited computing power has been a major factor in the dominance of algorithms that assume independent measurements. In the presence of merged measurements, these algorithms suffer from performance degradation, usually due to premature track termination. In this paper, we develop a principled Bayesian solution to this problem based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalised labelled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects. 2014 Conference Paper http://hdl.handle.net/20.500.11937/40489 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6916117 IEEE restricted |
| spellingShingle | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Multi-target tracking with merged measurements using labelled random finite sets |
| title | Multi-target tracking with merged measurements using labelled random finite sets |
| title_full | Multi-target tracking with merged measurements using labelled random finite sets |
| title_fullStr | Multi-target tracking with merged measurements using labelled random finite sets |
| title_full_unstemmed | Multi-target tracking with merged measurements using labelled random finite sets |
| title_short | Multi-target tracking with merged measurements using labelled random finite sets |
| title_sort | multi-target tracking with merged measurements using labelled random finite sets |
| url | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6916117 http://hdl.handle.net/20.500.11937/40489 |