Looking for Equivalence between Maximum Likelihood and Sparse DOA Estimators - Ifsttar
Poster De Conférence Année : 2024

Looking for Equivalence between Maximum Likelihood and Sparse DOA Estimators

Résumé

Direction-of-Arrival estimators depend on the regularization parameter λ which is often empirically tuned. In this work, conducted under the vectorized covariance matrix model, we are looking for theoretical equivalence between the Maximum Likelihood (ML) and sparse estimators. We show that under mild conditions, λ can be chosen thanks to the distribution of the minimum of the ML criterion in the case of two impinging sources. We derive this distribution under complex non-circular Gaussian noise. The corresponding λ choice is θ-invariant, only requiring an upper bound on the number of sources. Furthermore, it guarantees the global minimum of the sparse l0-regularized criterion to be the ML solution. Numerical experiments confirm that, for the proposed λ, sparse and ML estimators yield the same statistical performance.
Fichier principal
Vignette du fichier
EUSIPCO_2024_POSTER.pdf (851.06 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
licence

Dates et versions

hal-04669621 , version 1 (08-08-2024)

Licence

Identifiants

  • HAL Id : hal-04669621 , version 1

Citer

Thomas Aussaguès, Anne Ferréol, Alice Delmer, Pascal Larzabal. Looking for Equivalence between Maximum Likelihood and Sparse DOA Estimators. EUSIPCO 2024 - 32nd European Signal Processing Conference, Aug 2024, Lyon, France. . ⟨hal-04669621⟩
184 Consultations
45 Téléchargements

Partager

More