Abstract
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 |
|---|---|
| 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 |
|---|---|
| Country | New Zealand |
| City | Wellington |
| Period | 10/06/19 → 13/06/19 |
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Keywords
- optimisation
- filtering algorithms
- scheduling
- satellite observation
- genetic algorithm (GA)
Cite this
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Autonomous generation of observation schedules for tracking satellites with structured-chromosome GA optimisation. / Greco, Cristian; Gentile, Lorenzo; Filippi, Gianluca; Minisci, Edmondo; Vasile, Massimiliano; Bartz-Beielstein, Thomas.
2019 IEEE Congress on Evolutionary Computation. Piscataway, NJ : IEEE, 2019. p. 497-505 8790101.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Autonomous generation of observation schedules for tracking satellites with structured-chromosome GA optimisation
AU - Greco, Cristian
AU - Gentile, Lorenzo
AU - Filippi, Gianluca
AU - Minisci, Edmondo
AU - Vasile, Massimiliano
AU - Bartz-Beielstein, Thomas
N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2019/6/10
Y1 - 2019/6/10
N2 - 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.
AB - 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.
KW - optimisation
KW - filtering algorithms
KW - scheduling
KW - satellite observation
KW - genetic algorithm (GA)
U2 - 10.1109/CEC.2019.8790101
DO - 10.1109/CEC.2019.8790101
M3 - Conference contribution book
SN - 9781728121536
SP - 497
EP - 505
BT - 2019 IEEE Congress on Evolutionary Computation
PB - IEEE
CY - Piscataway, NJ
ER -