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Cross Spectrum (Complex » Continuous) (G Dataflow)

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Last Modified: June 25, 2019

Computes the averaged cross power spectrum of two signals.

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window parameter

A value that affects the output coefficients when window type is Kaiser, Gaussian, or Dolph-Chebyshev.

If window type is any other type of window, this node ignores this input.

This input represents the following information for each type of window:

  • Kaiser—Beta parameter
  • Gaussian—Standard deviation
  • Dolph-Chebyshev—The ratio of the main lobe to the side lobe, s, expressed in decibels

Default: NaN—Causes this node to set beta to 0 for a Kaiser window, the standard deviation to 0.2 for a Gaussian window, and s to 60 dB for a Dolph-Chebyshev window

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window type

Time-domain window to apply to the signal.

Name Value Description
Rectangle 0 Applies a rectangle window.
Hanning 1 Applies a Hanning window.
Hamming 2 Applies a Hamming window.
Blackman-Harris 3 Applies a Blackman-Harris window.
Exact Blackman 4 Applies an Exact Blackman window.
Blackman 5 Applies a Blackman window.
Flat Top 6 Applies a Flat Top window.
4 Term B-Harris 7 Applies a 4 Term B-Harris window.
7 Term B-Harris 8 Applies a 7 Term B-Harris window.
Low Sidelobe 9 Applies a Low Sidelobe window.
Blackman Nutall 11 Applies a Blackman Nutall window.
Triangle 30 Applies a Triangle window.
Bartlett-Hanning 31 Applies a Bartlett-Hanning window.
Bohman 32 Applies a Bohman window.
Parzen 33 Applies a Parzen window.
Welch 34 Applies a Welch window.
Kaiser 60 Applies a Kaiser window.
Dolph-Chebyshev 61 Applies a Dolph-Chebyshev window.
Gaussian 62 Applies a Gaussian window.
Force 64 Applies a Force window.
Exponential 65 Applies an Exponential window.

Default: Hanning

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restart averaging

A Boolean that specifies whether the node restarts the selected averaging process.

True Restarts the averaging process.
False Does not restart the averaging process.

When you call this node for the first time, the averaging process restarts automatically. A typical case when you restart averaging is when a major input change occurs in the middle of the averaging process.

Default: False

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signal x

First input signal.

This input accepts a waveform or a 1D array of double-precision, floating-point numbers.

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signal y

Second input signal.

This input accepts a waveform or a 1D array of double-precision, floating-point numbers.

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averaging parameters

Settings that define how this node computes the averaging.

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averaging mode

The mode this node uses to compute the averaging.

Name Description
No averaging Does not use averaging.
Vector averaging Uses vector averaging.
RMS averaging Uses RMS averaging.
Peak hold Uses peak hold averaging.

Default: No averaging

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weighting mode

Weighting mode for RMS and vector averaging.

Name Description
Linear Uses linear weighting.
Exponential Uses exponential weighting.

Default: Exponential

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number of averages

Number of averages to use for RMS and vector averaging.

If weighting mode is Exponential, the averaging process is continuous. If weighting mode is Linear, the averaging process stops after this node computes the specified number of averages.

Default: 10

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error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

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dt

Sample period of the time-domain signal in seconds.

Set this input to 1/fs, where fs is the sampling frequency of the time-domain signal.

This input is available only if you wire a 1D array of double-precision, floating-point numbers to signal x or signal y.

Default: 1

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cross spectrum

Averaged cross power spectrum of the input signals.

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f0

Start frequency, in Hz, of the spectrum.

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df

Frequency resolution, in Hz, of the spectrum.

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spectrum

Averaged cross power spectrum of the signals.

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averaging done

A Boolean that indicates whether the number of averages this node completed is greater than or equal to the specified number of averages.

True The number of averages this node completed is greater than or equal to the specified number of averages.
False The number of averages this node completed is less than the specified number of averages.

averaging done is True if averaging mode is No averaging.

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averages completed

Number of averages this node completed.

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error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Algorithm for Calculating the Cross Power Spectrum

The cross power, S x y ( f ) , of the signals x(t) and y(t) is defined as

S x y ( f ) = X * ( f ) Y ( f )

where

  • X*(f) is the complex conjugate of X(f)
  • X(f)=F{x(t)}
  • Y(f)=F{y(t)}

This node uses the FFT or DFT routine to compute the cross power spectrum, which is given by

S x y = 1 n 2 F * { X } F { Y }

where S x y represents the complex sequence cross spectrum and n is the number of samples that can accommodate input sequences signal x and signal y.

The largest cross power that this node can compute by the FFT is 223 (8,388,608 or 8M).

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Note

Some textbooks define the cross power spectrum as S ′ x y ( f ) = X ( f ) Y * ( f ) . If you prefer this definition of cross power to the one specified in this node, take the complex conjugate of the output sequence cross spectrum, because this node operates on the real and imaginary portions separately.

How the Number of Samples Affects this Node

When the number of samples in the inputs signal x and signal y are equal and are a valid power of 2, such that n = m = 2 k for k = 1, 2, 3,..., 23, this node makes direct calls to the FFT routine to compute the complex cross power sequence. This technique is efficient in both execution time and memory management because this node performs the operations in place.

When the number of samples in the inputs signal x and signal y are not equal, this node first resizes the smaller sequence by padding it with zeros to match the size of the larger sequence. If this size is a valid power of 2, such that max ⁡ ( n , m ) = 2 k for k = 1, 2, 3,..., 23, this node computes the cross power spectrum using the FFT. Otherwise, this node uses the slower DFT to compute the cross power spectrum. Thus, the size of the complex output sequence is defined by size = max ⁡ ( n , m ) .

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application

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