Robust Bayesian particle filter for space object tracking under severe uncertainty

Cristian Greco, Massimiliano Vasile

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
12 Downloads (Pure)


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 optimization. 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 a FENGYUN 1C debris in different operational instances, all characterized by a mix of aleatory and epistemic uncertainty on initial conditions and observation likelihoods.

Original languageEnglish
Pages (from-to)481-498
Number of pages18
JournalJournal of Guidance, Control and Dynamics
Issue number3
Early online date11 Nov 2021
Publication statusPublished - 31 Mar 2022


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


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