CrossSpectrum Estimator
Estimate crosspower spectrum density
Libraries:
DSP System Toolbox /
Estimation /
Power Spectrum Estimation
Description
The CrossSpectrum Estimator block estimates the crosspower spectrum density of two input signals, x and y, using Welch’s method of averaged modified periodograms. The block buffers the input data into overlapping segments. You can set the length of the data segment and the amount of data overlap between segments through the parameters in the block dialog box. The block computes the current power spectrum estimate by averaging the previous power spectrum estimates using the method you specify in the Averaging method parameter.
Ports
Input
x — First data input
vector  matrix
Specify the first data input as a vector or a matrix. The block treats each column of the input signal as a separate channel. If the input is a twodimensional signal, the first dimension represents the channel length (or frame size) and the second dimension represents the number of channels. If the input is a onedimensional signal, then the block interprets it as a singlechannel signal.
The inputs, x and y, must have the same size and data type.
Data Types: double
 single
Complex Number Support: Yes
y — Second data input
vector  matrix
Specify the second data input as a vector or a matrix. The block treats each column of the input signal as a separate channel. If the input is a twodimensional signal, the first dimension represents the channel length (or frame size) and the second dimension represents the number of channels. If the input is a onedimensional signal, then the block interprets it as a singlechannel signal.
The inputs, x and y, must have the same size and data type.
Data Types: double
 single
Complex Number Support: Yes
Output
Output — Crosspower spectrum density
vector  matrix
Crosspower spectrum density estimated by the block, returned as a vector or a matrix. The output has the same size and data type as the input signals.
Data Types: double
 single
Complex Number Support: Yes
Parameters
Window length source — Window length source
Same as input frame
length
(default)  Specify on dialog
Specify the source of the window length value as one of these:
Same as input frame length
(default) — Window length is equal to the frame size of the input.Specify on dialog
— Window length is the value specified in Window length.
Window length — Length of window in samples
1024
(default)  positive integer
Specify the length of the window in samples that the block uses to compute
the spectrum estimate as a positive integer greater than
2
.
Dependencies
To enable this parameter, set Window length
source to Specify on
dialog
.
Window overlap (%) — Percentage of overlap between windows
0
(default)  scalar in the range [0
,100
)
Specify the percentage of overlap between successive data windows as a
scalar in the range [0
,100
).
Averaging method — Averaging method
Running
(default)  Exponential
Specify the averaging method as:
Running
–– In the running averaging method, the block computes an equally weighted average of a specified number of spectrum estimates defined by the Number of spectral averages parameter.Exponential
–– In the exponential method, the block computes the average over samples weighted by an exponentially decaying forgetting factor.
Specify forgetting factor from input port — Specify forgetting factor from input port
off
(default)  on
Select this check box to specify the forgetting factor from an input port. When you do not select this check box, you must specify the forgetting factor through the Forgetting factor parameter in the block dialog box.
Dependencies
To enable this parameter, set Averaging method to
Exponential
.
Forgetting factor — Exponential weighting forgetting factor
0.9
(default)  scalar in the range (0
,1
]
Specify the exponential weighting forgetting factor as a scalar in the
range (0
,1
].
Dependencies
To enable this parameter, set Averaging method to
Exponential
and clear the
Specify forgetting factor from input port
parameter.
Number of spectral averages — Number of spectral averages
1
(default)  positive integer
Specify the number of spectral averages as a positive integer. The spectrum estimator computes the current power spectrum estimate by averaging the last N power spectrum estimates, where N is the number of spectral averages defined in Number of spectral averages.
Dependencies
To enable this parameter, set Averaging method to
Running
.
FFT length source — Source of FFT length value
Auto
(default)  Property
Specify the source of the FFT length value as one of these:
Auto
— FFT length is equal to the frame size of the input.Property
— FFT length is the value you specify in the FFT length parameter.
FFT length — FFT length
128
(default)  positive integer
Specify the FFT length the block uses to compute the spectral estimates as a positive integer.
Dependencies
To enable this parameter, set FFT length source
to Property
.
Window function — Window function
Hann
(default)  Chebyshev
 Flat Top
 Hamming
 Kaiser
 Rectangular
Specify the window function for the crossspectrum estimator as
Chebyshev
, Flat
Top
, Hamming
,
Hann
, Kaiser
, or
Rectangular
. The default value is
Hann
.
Sidelobe attenuation of window (dB) — Sidelobe attenuation of window in dB
60
(default)  positive scalar
Specify the sidelobe attenuation of the window as a positive scalar in dB.
Dependencies
To enable this parameter, set Window function to
Chebyshev
or
Kaiser
.
Frequency range — Frequency range of the crossspectrum estimate
Centered
(default)  Onesided
 Twosided
Specify the frequency range of the crossspectrum estimator as one of these:
Centered
— The crossspectrum estimator computes the centered twosided spectrum of complex or real input signals, x and y. The length of the crossspectrum estimate is equal to the FFT length. The block computes the spectrum estimate over the frequency range [SampleRate/2 SampleRate/2
], whereSampleRate
is the sample rate of the input signal.Onesided
— The crossspectrum estimator computes the onesided spectrum of real input signals, x and y. When the FFT length, NFFT is even, length of the crossspectrum estimate is (NFFT/2
) +1
, and is computed over the frequency range [0 SampleRate/2
]. When the FFT length, NFFT is odd, length of the crossspectrum estimate is (NFFT + 1)/2
, and is computed over the frequency range [0 SampleRate/2
].Twosided
— The crossspectrum estimator computes the twosided spectrum of complex or real input signals, x and y. The length of the crossspectrum estimate is equal to the FFT length. The spectrum estimate is computed over the frequency range [0 SampleRate
].
Inherit sample rate from input — Inherit sample rate from input
on
(default)  off
When you select this check box, the block computes the sample rate as N/Ts, where N is the frame size of the input signal, and Ts is the sample time of the input signal. When you clear this check box, the block uses the value you specify in Sample rate (Hz). By default, this check box is selected.
Sample rate (Hz) — Sample rate of input
44100
(default)  positive scalar
Specify the sample rate of the input signal as a positive scalar.
Dependencies
To enable this parameter, clear the Inherit sample rate from input check box.
Simulate using — Type of simulation to run
Interpreted execution
(default)  Code generation
Specify the type of simulation to run as one of these:
Interpreted execution
— Simulate model using the MATLAB^{®} interpreter. This option shortens startup time.Code generation
— Simulate model using generated C code. The first time you run a simulation, Simulink^{®} generates C code for the block. The C code is reused for subsequent simulations, as long as the model does not change. This option requires additional startup time but provides faster subsequent simulations.
Block Characteristics
Data Types 

Multidimensional Signals 

VariableSize Signals 

Algorithms
Welch's Method of Averaged Modified Periodograms
Give two signal inputs x and y:
Multiply the inputs by the window and scale the result by the window power.
Compute FFT of the signals X and Y, and multiply X with conj(Y) using
Z = X.*conj(Y)
.Compute the current crosspower spectrum estimate by taking the moving average of the last N number of Zs and scaling the answer by the sample rate.
For more information on the algorithms, see the Averaging Method section in Spectrum Analyzer.
References
[1] Hayes, Monson H. Statistical Digital Signal Processing and Modeling. Hoboken, NJ: John Wiley & Sons, 1996.
[2] Kay, Steven M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice Hall, 1999.
[3] Stoica, Petre, and Randolph L. Moses. Spectral Analysis of Signals. Englewood Cliffs, NJ: Prentice Hall, 2005.
[4] Welch, P. D. ''The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short Modified Periodograms''. IEEE Transactions on Audio and Electroacoustics. Vol. 15, No. 2, June 1967, pp. 70–73.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Version History
Introduced in R2015a
See Also
Objects
Blocks
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
 América Latina (Español)
 Canada (English)
 United States (English)
Europe
 Belgium (English)
 Denmark (English)
 Deutschland (Deutsch)
 España (Español)
 Finland (English)
 France (Français)
 Ireland (English)
 Italia (Italiano)
 Luxembourg (English)
 Netherlands (English)
 Norway (English)
 Österreich (Deutsch)
 Portugal (English)
 Sweden (English)
 Switzerland
 United Kingdom (English)