Autonomous generation of observation schedules for tracking satellites with structured-chromosome GA optimisation

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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.
LanguageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages497-505
Number of pages9
ISBN (Print)9781728121536
DOIs
Publication statusPublished - 10 Jun 2019
Event2019 IEEE Congress on Evolutionary Computation - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Conference

Conference2019 IEEE Congress on Evolutionary Computation
CountryNew Zealand
CityWellington
Period10/06/1913/06/19

Fingerprint

Chromosomes
Earth (planet)
Satellites
Space debris
State estimation
Global optimization
Mathematical operators
Orbits
Genetic algorithms
Scheduling

Keywords

  • optimisation
  • filtering algorithms
  • scheduling
  • satellite observation
  • genetic algorithm (GA)

Cite this

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title = "Autonomous generation of observation schedules for tracking satellites with structured-chromosome GA optimisation",
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.",
keywords = "optimisation, filtering algorithms, scheduling, satellite observation, genetic algorithm (GA)",
author = "Cristian Greco and Lorenzo Gentile and Gianluca Filippi and Edmondo Minisci and Massimiliano Vasile and Thomas Bartz-Beielstein",
note = "{\circledC} 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.",
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Greco, C, Gentile, L, Filippi, G, Minisci, E, Vasile, M & Bartz-Beielstein, T 2019, Autonomous generation of observation schedules for tracking satellites with structured-chromosome GA optimisation. in 2019 IEEE Congress on Evolutionary Computation., 8790101, IEEE, Piscataway, NJ, pp. 497-505, 2019 IEEE Congress on Evolutionary Computation, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8790101

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 proceedingConference contribution book

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