User`s guide

Burg AR Estimator
5-48
5Burg AR Estimator
Purpose Compute an estimate of AR model parameters using the Burg method.
Library Estimation / Parametric 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 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.
When
Inherit estimation order from input dimensions is selected, the
order, p, of the all-pole model is one less that the length of the input vector.
Otherwise, the order is the value specified by the
Estimation order parameter
The
Output(s) parameter allows you to select between two realizations of the
AR process:
•
A – 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
polynomial coefficients in descending powers of z,
[1 a(2) ... a(p+1)]
•K – The top output, K, is a column vector of length p with the same frame
status as the input, and contains 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).
Hz()
G
Az()
------------
G
1 a 2()z
1–
… ap 1+()z
p–
+++
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