LOW-ORDER COMPLEX AR MODELS FOR MEAN AND MAXIMUM FREQUENCY ESTIMATION IN THE CONTEXT OF DOPPLER COLOR FLOW MAPPING

Although autoregressive (AR) techniques are becoming increasingly popular in the field of Doppler ultrasound as an alternative form of spectral analysis to the conventional Fourier transform approach, their potential for evaluating specific features of the spectrum does not seem to have been explored so far. The present study attempts to investigate this aspect of AR techniques by developing mean and maximum frequency estimators suitable for use in Doppler color tiow mapping systems, where they are most needed. The estimators are based on low-order (for computational efficiency) AR models applied to complex signals whose real and imaginary parts are the in-phase and quadrature components of the analytical Doppler signal, respectively. A large number of simulated data sequences, generated by a sinusoidal computer model and having different number of samples, spectral shapes, bandwidths and SNR, are used to examine the performance (bias and variance) of the estimators in a systematic manner. Also, comparisons are made with the established autocorrelation technique, whose output is shown to be identical to one of the AR mean frequency estimators described in the paper. © 1990, IEEE