Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update
Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and leverages a joint prediction and update with Gibbs sampling to improve computationa...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/50659 |
| _version_ | 1848758514205327360 |
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| author | Jones, B. Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Jones, B. Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Jones, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and leverages a joint prediction and update with Gibbs sampling to improve computational efficiency. Based on the use of labeled random finite sets, the d-Generalized Labeled Multi-Bernoulli Filter provides a closed-form solution to the Bayes recursion for a multi-target filter. A similar filter, the Labeled Multi-Bernoulli Filter, is a principled approximation to reduce computational complexity. Upon combining these filters with astrodynamics-based models for orbit state probability density function prediction and initial orbit determination, a 100-object simulation is used to demonstrate the ability of these tools to track space objects in near-geosynchronous orbit. Both filters converge on solutions with sub-500 meter accuracy and demonstrate similar performance as a function of detection probability, clutter, and the birth model employed. A robust comparison of the two filters requires further Monte Carlo-based tests to quantify variance in the solutions due to random inputs. |
| first_indexed | 2025-11-14T09:45:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-50659 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:45:12Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-506592017-09-13T15:37:03Z Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update Jones, B. Vo, Ba Tuong Vo, Ba-Ngu Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and leverages a joint prediction and update with Gibbs sampling to improve computational efficiency. Based on the use of labeled random finite sets, the d-Generalized Labeled Multi-Bernoulli Filter provides a closed-form solution to the Bayes recursion for a multi-target filter. A similar filter, the Labeled Multi-Bernoulli Filter, is a principled approximation to reduce computational complexity. Upon combining these filters with astrodynamics-based models for orbit state probability density function prediction and initial orbit determination, a 100-object simulation is used to demonstrate the ability of these tools to track space objects in near-geosynchronous orbit. Both filters converge on solutions with sub-500 meter accuracy and demonstrate similar performance as a function of detection probability, clutter, and the birth model employed. A robust comparison of the two filters requires further Monte Carlo-based tests to quantify variance in the solutions due to random inputs. 2016 Conference Paper http://hdl.handle.net/20.500.11937/50659 10.2514/6.2016-5502 restricted |
| spellingShingle | Jones, B. Vo, Ba Tuong Vo, Ba-Ngu Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title | Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title_full | Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title_fullStr | Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title_full_unstemmed | Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title_short | Generalized labeled multi-Bernoulli space-object tracking with joint prediction and update |
| title_sort | generalized labeled multi-bernoulli space-object tracking with joint prediction and update |
| url | http://hdl.handle.net/20.500.11937/50659 |