Documentation

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 NameDefault ValueDescription

NSinusoids

2

Number of complex sinusoids

SegmentLength

4

Length of each of the time-based segments into which the input signal is divided.

OverlapPercent

50

Percent overlap between segments

WindowName

'Rectangular'

Window name string or 'User Defined' (see window for valid window names). For more information on each window, refer to its reference page.

This argument can also be a cell array containing the window name string or 'User Defined' and, if used for the particular window, an optional parameter value. The syntax is {wname,wparam}.

You can use set to change the value of the additional parameter or to define the MATLAB® expression and parameters for a user-defined window (see spectrum for information on using set).

SubspaceThreshold

0

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*threshold) are assigned to the noise subspace.

InputType

'Vector'

Type of input that will be used with this spectrum object. Valid values are 'Vector', 'DataMatrix' and 'CorrelationMatrix'.

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).

See Also

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