User`s guide

Modified Covariance Method
5-316
5Modified Covariance Method
Purpose Compute a parametric spectral estimate using the modified covariance
method.
Library Estimation / Power Spectrum Estimation
Description The Modified Covariance Method block estimates the power spectral density
(PSD) of the input using the modified covariance method. This method fits an
autoregressive (AR) model to the signal by minimizing the forward and
backward prediction errors in the least-squares sense. The order of the all-pole
model is the value specified by the
Estimation order parameter, and the
spectrum is computed from the FFT of the estimated AR model parameters.
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 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.
See the Burg Method block reference for a comparison of the Burg Method,
Covariance Method, Modified Covariance Method, and Yule-Walker Method
blocks.
Examples The dspsacomp demo compares the modified covariance method with several
other spectral estimation methods.