Burg AR Estimator (DSP Blockset)

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

Library

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).

Dialog Box

Output(s): The realization to output, model coefficients, reflection coefficients, or both.
Inherit estimation order from input dimensions: When selected, sets the estimation order p to one less than the length of the input vector.
Estimation order: The order of the AR model, p. This parameter is enabled when Inherit estimation order from input dimensions is not selected.

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.

Supported Data Types

Double-precision floating point
Single-precision floating point

To learn how to convert to the above data types in MATLAB and Simulink, see Supported Data Types and How to Convert to Them.

See Also

Burg Method
DSP Blockset

Covariance AR Estimator
DSP Blockset

Modified Covariance AR Estimator
DSP Blockset

Yule-Walker AR Estimator
DSP Blockset

arburg
Signal Processing Toolbox

Burg Method	DSP Blockset
Covariance AR Estimator	DSP Blockset
Modified Covariance AR Estimator	DSP Blockset
Yule-Walker AR Estimator	DSP Blockset
`arburg`	Signal Processing Toolbox

Also see Parametric Estimation for a list of all the blocks in the Parametric Estimation library.

Buffer Burg Method