Learn more about Signal Processing Blockset

Periodogram - Power spectral density or mean-square spectrum estimate using periodogram method

Library

Estimation / Power Spectrum Estimation

dspspect3

Description

The Periodogram block estimates the power spectral density (PSD) or mean-square spectrum (MSS) of the input. It does so by using the periodogram method and Welch's averaged, modified periodogram method. When you set the Number of spectral averages parameter to 1, the block computes the periodogram of the input. When the Number of spectral averages is greater than 1, the block uses the Welch method to compute a modified periodogram of the input. 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 M-by-N frame-based matrix input and M-by-N sample-based 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-by-N matrix output. When you select the Inherit FFT length from input dimensions check box, N_fft is 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, you can use the FFT length parameter to specify N_fft as a power of 2. The block either zero-pads or wraps the input to N_fft before computing the FFT.

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 output is always sample based.

When 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 time-domain 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.

The Window, 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.

Example

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.

Dialog Box

Measurement

Specify the type of measurement for the block to perform: Power spectral density or Mean-square spectrum. Tunable.

Window

Select the type of window to apply. See the Window Function block reference page for more details. Tunable.

Stopband attenuation in dB

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.

Beta

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.

Window sampling

From the list, choose Symmetric or Periodic. Tunable.

Inherit FFT length from input dimensions

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.

FFT length

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 zero-pads 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.

Number of spectral averages

Enter the number of spectra to average via Welch's Method; setting this parameter to 1 disables averaging.

Inherit sample time from 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 time-domain 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.

Sample time of original time series

Specify the sample time of the original time-domain signal. This parameter becomes visible only when you clear the Inherit sample time from input check box.

References

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.

Supported Data Types

Port	Supported Data Types
Input	Double-precision floating point Single-precision floating point
Output	Double-precision floating point Single-precision floating point

See Also

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
spectrum.periodogram	Signal Processing Toolbox
spectrum.welch	Signal Processing Toolbox

See Power Spectrum Estimation for related information.

Peak-Notch Filter

Permute Matrix

© 1984-2009- The MathWorks, Inc.    -   Site Help   -   Patents   -   Trademarks   -   Privacy Policy   -   Preventing Piracy   -   RSS