Kalman filter for radio astronomy dynamic imaging based on empirical covariances - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2024

Kalman filter for radio astronomy dynamic imaging based on empirical covariances

Résumé

Traditional Kalman filters are not immediately suitable for the estimation of state parameters with sample covariance matrices as observations. This comes from the fact that the observations must be first expressed as a linear function of sample covariance matrices. The primary objective of this work is to enable the calculation of both the mean and covariance of the observation model noise, which is here correlated with the state vector. Any signal distribution is considered, revealing the multivariate kurtosis of the signal in the measurement noise covariance matrix. The proposed method is evaluated on simulated data representative of a dynamic radio astronomy framework. The results show that our method is capable of effectively tracking moving sources in complex scenes with theoretical guaranties when the signal multivariate kurtosis is known.
Fichier principal
Vignette du fichier
2023_AEROSPACE (1).pdf (11.55 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04399504 , version 1 (17-01-2024)

Identifiants

  • HAL Id : hal-04399504 , version 1

Citer

Cyril Cano, Éric Chaumette, Pascal Larzabal, Mohammed Nabil EL KORSO, Isabelle Vin. Kalman filter for radio astronomy dynamic imaging based on empirical covariances. 2024. ⟨hal-04399504⟩
65 Consultations
10 Téléchargements

Partager

Gmail Facebook X LinkedIn More