Burg AR Estimator (DSP Blockset)

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

Library

Parametric Estimation, in Estimation

Description

The Burg AR Estimator block uses the Burg method to fit an autoregressive (AR) model to the input data by minimizing (least squares) the forward and backward prediction errors while constraining the AR parameters to satisfy the Levinson-Durbin recursion. 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 Output(s) parameter allows you to select between two realizations of the AR process:

A - The block outputs the normalized estimate of the AR model polynomial coefficients in descending powers of z,
K - The block outputs the reflection coefficients (which are a secondary result of the Levinson recursion).
A and K - The block outputs both realizations.

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

Dialog Box

Output(s): The realization to output, model coefficients or reflection coefficients.
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.