spectrum.cov (Signal Processing Toolbox)

Covariance spectrum

Syntax

Hs = spectrum.cov
Hs = spectrum.cov(order)
Hs = spectrum.cov(order,FFTLength)

Description

Hs = spectrum.cov returns a default covariance spectrum object, Hs, that defines the parameters for the covariance spectral estimation algorithm. The covariance algorithm estimates the spectral content by fitting an auto-regressive (AR) linear prediction model of a given order to the signal.

Hs = spectrum.cov(order) returns a spectrum object, Hs with the specified order and the FFTLength determined using NextPow2. The default value for order is 4.

Hs = spectrum.cov(order,FFTLength) returns a covariance spectrum object, Hs with the order of the covariance model and the specified way of determining the FFTLength. Valid values of the FFTLength string are:

FFTLength string
Description

'InputLength'
Use the length of the input signal as the FFT length

'NextPow2'
Use the next power of 2 greater than the input signal length as the FFT length. This is the default value.

'UserDefined'
Use the FFT length provided as an input to the psd method or via a dspopts object. See dspopts for an example.

FFTLength string	Description
`'InputLength'`	Use the length of the input signal as the FFT length
`'NextPow2'`	Use the next power of 2 greater than the input signal length as the FFT length. This is the default value.
`'UserDefined'`	Use the FFT length provided as an input to the `psd` method or via a `dspopts` object. See `dspopts` for an example.

Note See pcov for more information on the covariance algorithm.

Examples

Define a fourth order auto-regressive model and view its power spectral density using the covariance algorithm.

randn('state',1);
x=randn(100,1);
x=filter(1,[1 1/2 1/3 1/4 1/5],x);%Fourth order AR filter
Hs=spectrum.cov;                  %Fourth order AR model
psd(Hs,x,'NFFT',512)

spectrum.burg spectrum.eigenvector