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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

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
`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 λ_min , the smallest estimated eigenvalue of the signal's correlation matrix. Eigenvalues below the threshold (λ_min`*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.

randn('state',1);
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).

spectrum.eigenvector - Eigenvector spectrum

Syntax

Description

Examples

References

See Also

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