A Bayesian approach to ultrasound Doppler spectral analysis

Author

Giovannelli, Jean-Francois ; Herment, A. ; Demoment, G.

Author_Institution

CNRS, Gif-sur-Yvette

fYear

1993

fDate

31 Oct-3 Nov 1993

Firstpage

1055

Abstract

In this paper, we address the problem of power spectral density estimation of stationary Gaussian processes with Auto-Regressive (AR) models when only a short set of data is available for analysis. The AR coefficients are estimated through a regularized method proposed by Kitagawa and Gersch (1984). We describe an experimental study of this method and a comparison with the classical least squares (LS) method. This work is motivated by the excellent paper by Vaitkus et al (1988) whose purpose was to compare and assess classical spectral estimation methods - whether parametric or not - for biomedical applications in the field of Doppler ultrasound velocimetry

Keywords

Bayes methods; Doppler effect; acoustic signal processing; biomedical ultrasonics; least squares approximations; spectral analysis; Bayesian; Doppler ultrasound velocimetry; auto-regressive models; biomedical; power spectral density; stationary Gaussian processes; ultrasound Doppler spectral analysis; Bayesian methods; Context modeling; Covariance matrix; Gaussian processes; Least squares approximation; Least squares methods; Spectral analysis; Statistics; Ultrasonic imaging; Uninterruptible power systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Ultrasonics Symposium, 1993. Proceedings., IEEE 1993

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-2012-3

Type

conf

DOI

10.1109/ULTSYM.1993.339627

Filename

339627