Robust Bayesian particle filter for space object tracking under severe uncertainty

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Abstract

This paper introduces a robust Bayesian particle filter that can handle epistemic uncertainty in the measurements, dynamics, and initial conditions. The robust filter returns robust bounds on the output quantity of interest, rather than a crisp value. Particles are generated with an importance sampling technique and propagated only one time during the estimation process. The proposal distribution is constructed by running a parallel. Unscented Kalman Filter to drive particles in regions of high expected likelihood and achieve a high effective sample size. The bounds are then computed by an inexpensive tuning of the importance weights via numerical optimisation. A Branch&Bound algorithm over simplexes with a Lipschitz bounding function is employed to achieve guaranteed convergence to the lower and upper bounds in a finite number of steps. The filter is applied to the robust computation of the collision probability of SENTINEL 2B with aFENGYUN1C debris in different operational instances, all characterised by a mix of aleatory and epistemic uncertainty on initial conditions and observation likelihoods.
Original languageEnglish
Number of pages18
JournalJournal of Guidance, Control and Dynamics
Early online date11 Nov 2021
DOIs
Publication statusE-pub ahead of print - 11 Nov 2021

Keywords

  • Bayesian
  • particle filter
  • epistemic uncertainty
  • space object tracking
  • sever uncertainty

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