Efficient Evaluation of 2-D Collision Probability Derivatives for Uncertain k-scaled Covariances: A PcMax Case Study - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes
Pré-Publication, Document De Travail Année : 2024

Efficient Evaluation of 2-D Collision Probability Derivatives for Uncertain k-scaled Covariances: A PcMax Case Study

Résumé

This paper focuses on efficiently evaluating derivatives of the 2-D collision probability, treated as a function of parameters, which appear as linear forms in the entries of the covariance matrix. This boils down to computing moments of the associated Gaussian measure restricted to a disk. Specifically, we propose an optimization-based solution to computing the maximum collision probability when the covariance data is unreliable, implementing an alternative method to the traditional k-scaled covariance approach. Preliminary results indicate our method's potential for improving the understanding of Pc's validity as a measure of conjunction likelihood.
Fichier principal
Vignette du fichier
kpks_draft.pdf (380.49 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04710373 , version 1 (26-09-2024)

Identifiants

  • HAL Id : hal-04710373 , version 1

Citer

Denis Arzelier, Mioara Joldeş, Matthieu Masson. Efficient Evaluation of 2-D Collision Probability Derivatives for Uncertain k-scaled Covariances: A PcMax Case Study. 2024. ⟨hal-04710373⟩
0 Consultations
0 Téléchargements

Partager

More