Robust Bayesian particle filter for space object tracking under severe uncertainty

Research output: Contribution to journalArticlepeer-review

2 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 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
Publication statusE-pub ahead of print - 11 Nov 2021


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


Dive into the research topics of 'Robust Bayesian particle filter for space object tracking under severe uncertainty'. Together they form a unique fingerprint.

Cite this