spectrum.mcov (Signal Processing Toolbox)

Modified covariance spectrum

Syntax

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

Description

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

Hs = spectrum.mcov(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.mcov(order,FFTLength) returns a spectrum object, Hs with specified order and and the specified way of detrmining the FFTLength. Valid values of the FFTLength string are as follows:

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 pmcov for more information on the modified covariance algorithm.

Examples

Define a fourth order auto-regressive model and view its power spectral density using the modified 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.mcov;                 %Fourth order AR model
psd(Hs,x,'NFFT',512)

spectrum.eigenvector spectrum.mtm