This chapter presents a new robust particle filter that can handle set-valued probability measures. The method implemented in this new robust filter returns bounds on the probability associated with a given quantity of interest. The particles are generated with an importance sampling technique and propagated only once. The bounds are then computed by an inexpensive tuning of the importance weights. An optimisation procedure based on Branch & Bound which exploits the estimator finite derivative will be presented. The new robust particle filter is applied to the computation of the probability of collision of a piece of debris with an operational satellite in a scenario characterised by epistemic uncertainty on measurements and initial conditions.


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    Title :

    Robust Particle Filter for Space Navigation Under Epistemic Uncertainty


    Additional title:

    Space Technol.Proceedings



    Conference:

    International Conference on Uncertainty Quantification & Optimisation ; 2020 ; Brussels, Belgium November 17, 2020 - November 20, 2020



    Publication date :

    2021-07-16


    Size :

    19 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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