Robust particle filter for space objects tracking under severe uncertainty

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Abstract

This paper presents a robust particle filter approach able to handle a set-valued specification of the probability measures modelling the uncertainty structure of tracking problems. This method returns robust bounds on a quantity of interest compatibly with the infinite number of uncertain distributions specified. The importance particles are drawn and propagated only once, and the bound computation is realised by inexpensively tuning the importance weights. Furthermore, the uncertainty propagation is realised efficiently by employing an intrusive polynomial algebra technique. The developed method is finally applied to the computation of a debris-satellite collision probability in a scenario characterised by severe uncertainty.
Original languageEnglish
Number of pages20
Publication statusPublished - 15 Aug 2019
Event2019 AAS/AIAA Astrodynamics Specialist Conference - Portland, United States
Duration: 11 Aug 201915 Aug 2019

Conference

Conference2019 AAS/AIAA Astrodynamics Specialist Conference
CountryUnited States
CityPortland
Period11/08/1915/08/19

Keywords

  • uncertainty
  • bayesian statistics
  • collision avoidance
  • particle filter approach

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    Greco, C., Gentile, L., Vasile, M., Minisci, E., & Bartz-Beielstein, T. (2019). Robust particle filter for space objects tracking under severe uncertainty. Paper presented at 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, United States.