User`s guide

Burg Method
5-50
5Burg Method
Purpose Compute a parametric spectral estimate using the Burg method.
Library Estimation / Power Spectrum Estimation
Description The Burg Method block estimates the power spectral density (PSD) of the input
frame using the Burg method. This method fits an autoregressive (AR) model
to the signal 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. The block’s output (a column vector) is the estimate of
the signal’s power spectral density at N
fft
equally spaced frequency points in
the range [0,F
s
), where F
s
is the signal’s sample frequency.
When
Inherit estimation order from input dimensions is selected, the order
of the all-pole model is one less that the input frame size. Otherwise, the order
is the value specified by the
Estimation order parameter. The spectrum is
computed from the FFT of the estimated AR model parameters.
When
Inherit FFT length from input dimensions is selected, N
fft
is specified
by the frame size of the input, which must be a power of 2. When
Inherit FFT
length from input dimensions
is not selected, N
fft
is specified as a power of 2
by the
FFT length parameter, and the block zero pads or truncates the input
to N
fft
before computing the FFT. The output is always sample-based.
The Burg Method and Yule-Walker Method blocks return similar results for
large frame sizes. The following table compares the features of the Burg
Method block to the Covariance Method, Modified Covariance Method, and
Yule-Walker Method blocks.