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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.
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.
See Also
arcov |
Compute an estimate of AR model parameters using the covariance method. |
armcov |
Compute an estimate of AR model parameters using the modified covariance method. |
aryule |
Compute an estimate of AR model parameters using the Yule-Walker method. |
lpc |
Linear prediction coefficients. |
pburg |
Power spectrum estimate using the Burg method. |
prony |
Prony's method for time domain IIR filter design. |