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Abstract
This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces.
The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.
The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.
Original language | English |
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Title of host publication | 2019 IEEE Congress on Evolutionary Computation |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 497-505 |
Number of pages | 9 |
ISBN (Print) | 9781728121536 |
DOIs | |
Publication status | Published - 10 Jun 2019 |
Event | 2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019 |
Conference
Conference | 2019 IEEE Congress on Evolutionary Computation |
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Abbreviated title | IEEE CEC 2019 |
Country | New Zealand |
City | Wellington |
Period | 10/06/19 → 13/06/19 |
Keywords
- optimisation
- filtering algorithms
- scheduling
- satellite observation
- genetic algorithm (GA)
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