Asymptotic performance of second-order algorithms - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Signal Processing Année : 2002

Asymptotic performance of second-order algorithms

Résumé

This paper reexamines the asymptotic performance analysis of second-order methods for parameter estimation in a general context. It provides a unifying framework to investigate the asymptotic performance of second-order methods under the stochastic model assumption in which both the waveforms and noise signals are possibly temporally correlated, possibly non-Gaussian, real or complex (possibly noncircular) random processes. Thanks to a functional approach and a matrix valued reformulated central limit theorem about the sample covariance matrix, the conditions under which the asymptotic covariance of a parameter estimator are dependent or independent of the distribution of the signal involved are specified. Finally, we demonstrate the application of our general results, to direction of arrival (DOA) estimation, identification of finite impulse response models, sinusoidal frequency estimation for mixed spectra time series and frequency estimation of sinusoidal signal with very lowpass envelope.
Fichier principal
Vignette du fichier
17.pdf (359.67 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03399913 , version 1 (24-10-2021)

Identifiants

  • HAL Id : hal-03399913 , version 1

Citer

Jean-Pierre Delmas. Asymptotic performance of second-order algorithms. IEEE Transactions on Signal Processing, 2002. ⟨hal-03399913⟩
14 Consultations
32 Téléchargements

Partager

Gmail Facebook X LinkedIn More