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Physics and human-based information fusion for improved resident space object tracking
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Delande, Emmanuel, Houssineau, Jeremie and Jah, Moriba (2018) Physics and human-based information fusion for improved resident space object tracking. Advances in Space Research, 62 (7). pp. 1800-1812. doi:10.1016/j.asr.2018.06.033 ISSN 0273-1177.
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Official URL: http://dx.doi.org/10.1016/j.asr.2018.06.033
Abstract
Maintaining a catalog of Resident Space Objects (RSOs) can be cast in a typical Bayesian multi-object estimation problem, where the various sources of uncertainty in the problem – the orbital mechanics, the kinematic states of the identified objects, the data sources, etc. – are modeled as random variables with associated probability distributions. In the context of Space Situational Awareness, however, the information available to a space analyst on many uncertain components is scarce, preventing their appropriate modeling with a random variable and thus their exploitation in a RSO tracking algorithm. A typical example are human-based data sources such as Two-Line Elements (TLEs), which are publicly available but lack any statistical description of their accuracy. In this paper, we propose the first exploitation of uncertain variables in a RSO tracking problem, allowing for a representation of the uncertain components reflecting the information available to the space analyst, however scarce, and nothing more. In particular, we show that a human-based data source and a physics-based data source can be embedded in a unified and rigorous Bayesian estimator in order to track a RSO. We illustrate this concept on a scenario where real TLEs queried from the U.S. Strategic Command are fused with realistically simulated radar observations in order to track a Low-Earth Orbit satellite.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Advances in Space Research | ||||||||
Publisher: | Elsevier Science BV | ||||||||
ISSN: | 0273-1177 | ||||||||
Official Date: | 1 October 2018 | ||||||||
Dates: |
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Volume: | 62 | ||||||||
Number: | 7 | ||||||||
Page Range: | pp. 1800-1812 | ||||||||
DOI: | 10.1016/j.asr.2018.06.033 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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