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

Cristian Greco, Lorenzo Gentile, Gianluca Filippi, Edmondo Minisci, Massimiliano Vasile, Thomas Bartz-Beielstein

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

8 Citations (Scopus)
25 Downloads (Pure)


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


Conference2019 IEEE Congress on Evolutionary Computation
Abbreviated titleIEEE CEC 2019
Country/TerritoryNew Zealand


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


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