Random finite set multi-target trackers: Stochastic geometry for space situational awareness
© 2015 SPIE. This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchma...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/46641 |
| _version_ | 1848757616240492544 |
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| author | Vo, Ba Tuong Vo, B. |
| author_facet | Vo, Ba Tuong Vo, B. |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 SPIE. This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchmark for the RFS approach. Since there is a suite of RFS-based multi-target tracking algorithms, benchmarking tracking performance of the RFS approach by using the PHD filter, the cheapest of these, is misleading. Such benchmarking should be performed with more sophisticated RFS algorithms. In this paper we outline the high-performance RFS-based multi-target trackers such that the Generalized Labled Multi-Bernoulli filter, and a number of efficient approximations and discuss extensions and applications of these filters. Applications to space situational awareness are discussed. |
| first_indexed | 2025-11-14T09:30:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-46641 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:30:55Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-466412017-09-13T14:08:02Z Random finite set multi-target trackers: Stochastic geometry for space situational awareness Vo, Ba Tuong Vo, B. © 2015 SPIE. This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchmark for the RFS approach. Since there is a suite of RFS-based multi-target tracking algorithms, benchmarking tracking performance of the RFS approach by using the PHD filter, the cheapest of these, is misleading. Such benchmarking should be performed with more sophisticated RFS algorithms. In this paper we outline the high-performance RFS-based multi-target trackers such that the Generalized Labled Multi-Bernoulli filter, and a number of efficient approximations and discuss extensions and applications of these filters. Applications to space situational awareness are discussed. 2015 Conference Paper http://hdl.handle.net/20.500.11937/46641 10.1117/12.2180839 restricted |
| spellingShingle | Vo, Ba Tuong Vo, B. Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title | Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title_full | Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title_fullStr | Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title_full_unstemmed | Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title_short | Random finite set multi-target trackers: Stochastic geometry for space situational awareness |
| title_sort | random finite set multi-target trackers: stochastic geometry for space situational awareness |
| url | http://hdl.handle.net/20.500.11937/46641 |