Signal Processing Toolbox
arburg

Compute an estimate of AR model parameters using the Burg method

Syntax

• ```a` = `arburg(x,p)
[a,e]` = `arburg(x,p)
[a,e,k]` = `arburg(x,p)
```

Description

```a = arburg(x,p) ``` uses the Burg method to fit a `p`th order autoregressive (AR) model to the input signal, `x`, by minimizing (least squares) the forward and backward prediction errors while constraining the AR parameters to satisfy the Levinson-Durbin recursion. `x` is assumed to be the output of an AR system driven by white noise. Vector `a` contains the normalized estimate of the AR system parameters, A(z), in descending powers of z.

Since the method characterizes the input data using an all-pole model, the correct choice of the model order `p` is important.

```[a,e] = arburg(x,p) ``` returns the variance estimate, `e`, of the white noise input to the AR model.

```[a,e,k] = arburg(x,p) ``` returns a vector, `k`, of reflection coefficients.

`arcov`, `armcov`, `aryule`, `lpc`, `pburg`, `prony`