Covariance AR Estimator (DSP Blockset)

Compute an estimate of AR model parameters using the covariance method.

Library

Parametric Estimation, in Estimation

Description

The Covariance AR Estimator block uses the covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward prediction error in the least-squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.

The order, p, of the all-pole model is specified by the Order parameter.

The top output, A, contains the normalized estimate of the AR model coefficients in descending powers of z,

[1 a(2) ... a(p+1)]

The scalar gain, G, is provided at the bottom output (G).

Dialog Box

Input frame size: The number of samples in the input frame.
Order: The order of the AR model.

References

Kay, S. M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.

Marple, S. L., Jr., Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: Prentice-Hall, 1987.