Estimate crosspower spectrum density
Estimation / Power Spectrum Estimation
dspspect3
The CrossSpectrum Estimator block outputs the frequency crosspower spectrum density of two real or complex input signals, x and y, via Welch’s method of averaged modified periodograms. The input signals must be of the same size and data type.
The CrossSpectrum Estimator block 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. The block buffers the input data into overlapping segments. You can set the length of the data segment and the amount of data overlap through the parameters set in the block dialog box. The block computes the power spectrum based on the parameters set in the block dialog box.
Each column of the input signal is treated 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 it is interpreted as a single channel.
Source of the window length value. You can set this parameter to:
Same as input frame length
(default) — Window length is set to the frame size of the
input.
Specify on dialog
— Window
length is the value specified in Window
length.
This parameter is nontunable.
Length of the window, in samples, used to compute the spectrum estimate,
specified as a positive integer scalar greater than 2
.
This parameter applies when you set Window length
source to Specify on dialog
. The
default is 1024
. This parameter is nontunable.
Percentage of overlap between successive data windows, specified as a
scalar in the range [0, 100
). The default is
0
. This parameter is nontunable.
Specify the averaging method as Running
or
Exponential
. 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. In the exponential method, the block
computes the average over samples weighted by an exponentially decaying
forgetting factor.
Number of spectral averages, specified as a positive integer scalar. The
default is 1
. 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. This
parameter is nontunable.
This parameter applies when Averaging method is set
to Running
.
Select this check box to specify the forgetting factor from an input port. When you do not select this check box, the forgetting factor is specified through the Forgetting factor parameter.
This parameter applies when Averaging method is set
to Exponential
.
Specify the exponential weighting forgetting factor as a scalar value
greater than zero and smaller than or equal to one. The default is
0.9
.
This parameter applies when you set Averaging method
to Exponential
and clear the Specify
forgetting factor from input port parameter.
Source of the FFT length value. You can set this parameter to:
Auto
(default) — FFT length
is set to the frame size of the input.
Property
— FFT length is the
value specified in FFT length.
This parameter is nontunable.
Length of the FFT used to compute the spectrum estimates, specified as a
positive integer scalar. This parameter applies when you set FFT
length source to Property
. The
default is 1024
. This parameter is nontunable.
Window function for the crossspectrum estimator, specified as one of
Chebyshev
 Flat
Top
 Hamming

Hann
 Kaiser

Rectangular
. The default is
Hann
. This parameter is nontunable.
Side lobe attenuation of the window, specified as real positive scalar.
This parameter applies when you set Window function to
Chebyshev
or
Kaiser
. The default is 60
.
This parameter is nontunable.
Frequency range of the crossspectrum estimator. You can set this parameter to:
centered
(default) — 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 spectrum estimate is
computed over the frequency range [SampleRate/2
SampleRate/2]
when the FFT length is even and
[SampleRate/2 SampleRate/2]
when FFT
length is odd.
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]
, where SampleRate
is the sample rate of the input signal.
This parameter is nontunable.
When you select this check box, the block’s sample rate is computed 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 sample rate is the value specified in Sample rate (Hz). By default, this check box is selected.
Sample rate of the input signal, specified as a positive scalar value. The
default is 44100
. This parameter applies when you clear
the Inherit sample rate from input check box. This
parameter is nontunable.
Type of simulation to run. You can set this parameter to:
Code generation
(default)
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 simulation speed
than Interpreted
execution
.
Interpreted execution
Simulate model using the MATLAB^{®} interpreter. This option shortens startup
time but has slower simulation speed than Code
generation
.
Port  Supported Data Types 

Input 

Output 

[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.