User`s guide

Covariance AR Estimator
5-111
5Covariance AR Estimator
Purpose Compute an estimate of AR model parameters using the covariance method.
Library Estimation / Parametric 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 sample-based vector (row, column, or 1-D) or frame-based vector
(column only) representing a frame of consecutive time samples from a
single-channel signal, 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 frame.
The order, p, of the all-pole model is specified by the
Estimation order
parameter.
The top output,
A, is a column vector of length p+1 with the same frame status
as the input, and 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
Estimation order
The order of the AR model, p.
Hz()
G
Az()
------------
G
1 a 2()z
1–
… ap 1+()z
p–
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