| Signal Processing Blockset™ | ![]() |
Estimation / Power Spectrum Estimation
dspspect3

The Periodogram block computes a nonparametric estimate of the spectrum. The block averages the squared magnitude of the FFT computed over windowed sections of the input and normalizes the spectral average by the square of the sum of the window samples.
Both an M-by-N frame-based matrix input and an M-by-N sample-based matrix input are treated 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 Nfft-by-N matrix output. When you select the Inherit FFT length from input dimensions check box, Nfft is specified by the frame size of the input, which must be a power of 2. When you clear the Inherit FFT length from input dimensions check box, Nfft is specified as a power of 2 by the FFT length parameter, and the block zero pads or wraps the input to Nfft before computing the FFT.
Each column of the output matrix contains the estimate of the corresponding input column's power spectral density at Nfft equally spaced frequency points in the range [0,Fs), where Fs is the signal's sample frequency. The output is always sample based.
The Number of spectral averages specifies the number of spectra to average. Setting this parameter to 1 effectively disables averaging.
The Window type, Stopband ripple, Beta, and Window sampling parameters all apply to the specification of the window function; see the Window Function block reference page for more details on these four parameters.
The dspstfft demo provides an illustration of using the Periodogram and Matrix Viewer blocks to create a spectrogram. The dspsacomp demo compares the Periodogram block with several other spectral estimation methods.

Enter the type of window to apply. See the Window Function block reference page for more details. Tunable.
Enter the level, in dB, of stopband attenuation, Rs, for the Chebyshev window. This parameter is enabled if, for the Window type parameter, you choose Chebyshev. Tunable.
Enter the β parameter for the Kaiser window. This parameter is enabled if, for the Window type parameter, you chose Kaiser. Increasing Beta widens the mainlobe and decreases the amplitude of the window sidelobes in the window's frequency magnitude response. Tunable.
From the list, choose Symmetric or Periodic. Tunable.
When you select this check box, the block uses the input frame size as the number of data points, Nfft, on which to perform the FFT.
Enter the number of data points on which to perform the FFT, Nfft. When Nfft is larger than the input frame size, each frame is zero-padded as needed. When Nfft is smaller than the input frame size, each frame is wrapped as needed. This parameter is enabled when you clear the Inherit FFT length from input dimensions check box.
Enter the number of spectra to average; setting this parameter to 1 disables averaging.
Oppenheim, A. V. and R. W. Schafer. Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.
Orfanidis, S. J. Introduction to Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1995.
Proakis, J. and D. Manolakis. Digital Signal Processing. 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
| Port | Supported Data Types |
|---|---|
Input |
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Output |
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| Burg Method | Signal Processing Blockset |
| Inverse Short-Time FFT | Signal Processing Blockset |
| Magnitude FFT | Signal Processing Blockset |
| Short-Time FFT | Signal Processing Blockset |
| Spectrum Scope | Signal Processing Blockset |
| Window Function | Signal Processing Blockset |
| Yule-Walker Method | Signal Processing Blockset |
| pwelch | Signal Processing Toolbox |
See Power Spectrum Estimation for related information.
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