Power spectral density or meansquare spectrum estimate using periodogram method
Estimation / Power Spectrum Estimation
dspspect3
The Periodogram block estimates the power spectral density (PSD) or meansquare spectrum (MSS) of the input. It does so by using the periodogram method and Welch's averaged, modified periodogram method. The block averages the squared magnitude of the FFT computed over windowed sections of the input. It then normalizes the spectral average by the square of the sum of the window samples. See Periodogram and Welch's Method in the Signal Processing Toolbox™ documentation for more information.
The block treats MbyN framebased matrix input and MbyN samplebased matrix input as M sequential time samples from N independent channels. The block computes a separate estimate for each of the N independent channels and generates an N_{fft}byN matrix output.
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 always outputs samplebased data.
Specify the type of measurement for the block to perform: Power
spectral density
or Meansquare spectrum
. Tunable.
Select the type of window to apply. See the Window Function block reference page for more details. Tunable.
Enter the level, in decibels (dB), of stopband attenuation,
R_{s}, for the Chebyshev window. This parameter
becomes visible if, for the Window parameter,
you choose Chebyshev
. Tunable.
Enter the β parameter for the Kaiser window. This parameter
becomes visible if, for the Window parameter,
you chose Kaiser
. Increasing Beta widens
the mainlobe and decreases the amplitude of the sidelobes in the displayed
frequency magnitude response. Tunable. See the Window
Function block reference page for more details.
From the list, choose Symmetric
or Periodic
.
See the Window Function block reference
page for more details.Tunable.
Set this parameter to FFTW
[1], [2] to
support an arbitrary length input signal. The block restricts generated
code with FFTW implementation to MATLAB^{®} host computers.
Set this parameter to Radix2
for bitreversed
processing, fixed or floatingpoint data, or for portable Ccode generation
using the 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 let the
block choose the FFT implementation. For nonpoweroftwo transform
lengths, the block restricts generated code to MATLAB host computers.
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 estimation order check box. You can then specify a power of two FFT length using the FFT length parameter.
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. This parameter becomes visible only when you clear the Inherit FFT length from input dimensions check box.
When you set the FFT implementation parameter
to Radix2
, this value must be a power of two.
Specify the number of spectra 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.
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 using the Sample time of original time series parameter.
Specify the sample time of the original timedomain signal. This parameter becomes visible only when you clear the Inherit sample time from input check box.
The dspstfft
example
provides an illustration of using the Periodogram and Matrix Viewer
blocks to create a spectrogram. The dspsacomp
example
compares the Periodogram block with several other spectral estimation
methods.
Oppenheim, A. V. and R. W. Schafer. DiscreteTime Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.
Orfanidis, S. J. Introduction to Signal Processing. Englewood Cliffs, NJ: PrenticeHall, 1995.
Proakis, J. and D. Manolakis. Digital Signal Processing. 3rd ed. Englewood Cliffs, NJ: PrenticeHall, 1996.
[1] FFTW (http://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.
Port  Supported Data Types 

Input 

Output 

Burg Method  DSP System Toolbox 
Inverse ShortTime FFT  DSP System Toolbox 
Magnitude FFT  DSP System Toolbox 
ShortTime FFT  DSP System Toolbox 
Spectrum Analyzer  DSP System Toolbox 
Window Function  DSP System Toolbox 
YuleWalker Method  DSP System Toolbox 
See Spectral Analysisfor related information.