Periodogram
Power spectral density or meansquare spectrum estimate using periodogram method
Libraries:
DSP System Toolbox /
Estimation /
Power Spectrum Estimation
Description
The Periodogram block estimates the power spectral density (PSD) or meansquare spectrum (MSS) of the input. The block uses the periodogram method and Welch's averaged, modified periodogram method. The block averages the squared magnitude of the FFT function computed over windowed sections of the input. It then normalizes the spectral average by the square of the sum of the window samples. For more information, see Periodogram and Welch's Method.
Examples
Compute Periodogram of Signal
Using the Periodogram block, compute the periodogram of signal.
Ports
Input
Port_1 — Input signal
vector  matrix
Input signal, specified as a vector or a matrix.
The block treats MbyN matrix input as M sequential time samples from N independent channels.
Data Types: single
 double
Complex Number Support: Yes
Output
Port_1 — Output signal
vector  matrix
Output signal, returned as a vector or a matrix.
Each column of the output matrix contains the estimate of the power spectral density of the corresponding input column at N_{fft} equally spaced frequency points. The frequency points are in the range [0,F_{s}), where F_{s} is the sampling frequency of the signal.
The block computes a separate estimate for each of the N independent channels and generates an N_{fft}byN matrix output.
Data Types: single
 double
Parameters
Measurement — Measurement type
Power spectral
density
(default)  Meansquare
spectrum
Specify the type of measurement for the
block to perform: Power spectral
density
or Meansquare
spectrum
.
Tunable: No
Window — Window type
Hamming
(default)  Bartlett
 Blackman
 Boxcar
 Chebyshev
 Hann
 Hanning
 Kaiser
 Triang
Select the type of window to apply.
For details, see the Window Function block reference page.
Tunable: Yes
Beta — Beta
5
(default)  scalar
Enter the β parameter for the Kaiser window. Increasing Beta widens the mainlobe and decreases the amplitude of the sidelobes in the displayed frequency magnitude response.
For more details, see the Window Function block reference page.
Tunable: Yes
Dependencies
This parameter becomes visible if, for the
Window
parameter, you chose
Kaiser
.
Window sampling — Window sampling
Symmetric
(default)  Periodic
From the list, choose
Symmetric
or
Periodic
.
See the Window Function block reference page for more details.
Tunable: Yes
Dependencies
This parameter becomes visible only if you
set the Window
parameter to one of
Blackman
,
Hamming
,
Hann
, or
Hanning
.
FFT implementation — FFT implementation
Auto
(default)  Radix2
 FFTW
Set this parameter to
FFTW
to support an
arbitrary length input signal. The block restricts
generated code with an FFTW implementation to
MATLAB^{®} host computers.
Set this parameter to
Radix2
for
bitreversed processing, fixed or floatingpoint
data, or for portable Ccode generation by using
Simulink^{®}
Coder™. The first dimension
M of the input matrix must be
a power of two. To work with other input sizes,
use the Pad block
to pad or truncate these dimensions to powers of
two, or if possible, choose the FFTW
implementation.
Set this parameter to
Auto
to enable the
block choose the FFT implementation. For
nonpoweroftwo transform lengths, the block
restricts generated code to MATLAB host computers.
Tunable: No
Stopband attenuation in dB — Stopband attenuation in dB
50
(default)  scalar
Enter the level, in decibels (dB), of stopband attenuation, R_{s}, for the Chebyshev window.
Tunable: Yes
Dependencies
This parameter becomes visible if, for the
Window
parameter, you choose
Chebyshev
.
Inherit FFT length from input dimensions — Inherit FFT length
off
(default)  on
When you select this check box, the block uses the input frame size as the number of data points, N_{fft}, on which to perform the FFT. To specify the number of points on which to perform the FFT, clear the Inherit FFT length from input dimensions check box. You can then specify a power of two FFT length by using the FFT length parameter.
Tunable: No
FFT length — Number of data points
256
(default)  scalar
Enter the number of data points on which to perform the FFT, N_{fft}. When N_{fft} is larger than the input frame size, the block zeropads each frame as needed. When N_{fft} is smaller than the input frame size, the block wraps each frame as needed.
When you set the FFT implementation
parameter to
Radix2
, this value
must be a power of two.
Tunable: No
Dependencies
This parameter becomes visible only when you clear the Inherit FFT length from input dimensions check box.
Number of spectral averages — Number of spectral averages
4
(default)  scalar
Specify the number of spectral to average.
When you set this value to 1
,
the block computes the periodogram of the input.
When you set this value greater
1
, the block implements Welch's Method to compute a modified
periodogram of the input.
Tunable: No
Inherit sample time from input — Inherit sample time from input
on
(default)  off
If you select the Inherit sample time from input check box, the block computes the frequency data from the sample period of the input signal. For the block to produce valid output, the following conditions must hold:
The input to the block is the original signal, with no samples added or deleted (by insertion of zeros, for example).
The sample period of the timedomain signal in the simulation equals the sample period of the original time series.
If these conditions do not hold, clear the
Inherit sample time from
input check box. You can then specify a
sample time by using the Sample time of original time series
parameter.
Dependencies
This parameter becomes visible only if you
set the Measurement
parameter to
Power spectral
density
.
Sample time of original time series — Sample time of original time series
1
(default)  scalar
Specify the sample time of the original timedomain signal.
Tunable: No
Dependencies
This parameter becomes visible only when you
clear the Inherit sample time from input
check
box.
Block Characteristics
Data Types 

Multidimensional Signals 

VariableSize Signals 

References
[1] FFTW (
https://www.fftw.org
)
[2] Frigo, M. and S. G. Johnson, “FFTW: An Adaptive Software Architecture for the FFT,” Proceedings of the International Conference on Acoustics, Speech, and Signal Processing , Vol. 3, 1998, pp. 13811384.
[3] Oppenheim, A. V. and R. W. Schafer. DiscreteTime Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.
[4] Orfanidis, S. J. Introduction to Signal Processing. Englewood Cliffs, NJ: PrenticeHall, 1995.
[5] Proakis, J. and D. Manolakis. Digital Signal Processing. 3rd ed. Englewood Cliffs, NJ: PrenticeHall, 1996.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Usage notes and limitations:
Generated code relies on the
memcpy
ormemset
function (string.h
) under certain conditions.When the following conditions apply, the executable generated from this block relies on prebuilt dynamic library files (
.dll
files) included with MATLAB:FFT implementation is set to
FFTW
.Inherit FFT length from input dimensions is cleared and FFT length is set to a value that is not a power of two.
Use the
packNGo
function to package the code generated from this block and all the relevant files in a compressed zip file. Using this zip file, you can relocate, unpack, and rebuild your project in another development environment where MATLAB is not installed. For more details, see How To Run a Generated Executable Outside MATLAB.When the FFT length is a power of two, you can generate standalone C and C++ code from this block.
Version History
Introduced before R2006a
MATLAB 명령
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