spectrum.eigenvector
Eigenvector spectrum
Syntax
Hs = spectrum.eigenvector
Hs = spectrum.eigenvector(NSinusoids)
Hs = spectrum.eigenvector(NSinusoids,SegmentLength)
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent)
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName)
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName,SubspaceThreshold)
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName,SubspaceThreshold,InputType)
Description
Note
The use of spectrum.eigenvector
is not
recommended. Use peig
instead.
Hs = spectrum.eigenvector
returns
a default eigenvector spectrum object, Hs
, that
defines the parameters for an eigenanalysis spectral estimation method.
This object uses the following default values:
Default Values
Property Name | Default Value | Description |
---|---|---|
|
| Number of complex sinusoids |
|
| Length of each of the time-based segments into which the input signal is divided. |
|
| Percent overlap between segments |
|
| Window name or This argument can also be a cell
array containing the window name or You can use
|
|
| Threshold is the cutoff for signal and noise separation.
The threshold is multiplied by λmin ,
the smallest estimated eigenvalue of the signal's correlation matrix.
Eigenvalues below the threshold (λmin |
|
| Type of input that will be used with this spectrum object.
Valid values are |
Hs = spectrum.eigenvector(NSinusoids)
returns
a spectrum object, Hs
, with the specified number
of sinusoids and default values for all other properties. Refer to
the table above for default values.
Hs = spectrum.eigenvector(NSinusoids,SegmentLength)
returns
a spectrum object, Hs
, with the specified segment
length.
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent)
returns a spectrum object, Hs
, with the specified overlap between
segments.
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName)
returns a spectrum object, Hs
, with the specified window.
Note
Window names must be enclosed in single quotes, such as spectrum.eigenvector(3,32,50,'chebyshev')
or spectrum.eigenvector(3,32,50,{'chebyshev',60})
.
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName,SubspaceThreshold)
returns a spectrum object, Hs
, with the specified subspace
threshold.
Hs = spectrum.eigenvector(NSinusoids,SegmentLength,...
OverlapPercent,WindowName,SubspaceThreshold,InputType)
returns a spectrum object, Hs
, with the specified input type.
Note
See peig
for more information
on the eigenanalysis algorithm.
Examples
Define a complex signal with three sinusoids, add noise, and view its pseudospectrum using eigenanalysis. Set the FFT length to 128.
n=0:99; s=exp(i*pi/2*n)+2*exp(i*pi/4*n)+exp(i*pi/3*n)+randn(1,100); Hs=spectrum.eigenvector(3,32,95,'rectangular',5); pseudospectrum(Hs,s,'NFFT',128)
References
[1] Harris, F. J. “On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform.” Proceedings of the IEEE®. Vol. 66 (January 1978).
Version History
Introduced before R2006a