A Bayesian approach to ultrasound Doppler spectral analysis

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