Estimate power spectrum or power density spectrum
The dsp.SpectrumEstimator
computes the power
spectrum or the power density spectrum of a signal, using the Welch
algorithm and the filter bank approach.
When you choose the Welch method, the object computes the averaged modified periodograms to compute the spectral estimate. When you choose the filter bank approach, an analysis filter bank splits the broadband input signal into multiple narrow subbands. The object computes the power in each narrow frequency band and the computed value is the spectral estimate over the respective frequency band. For signals with relatively small FFT lengths, the filter bank approach produces a spectral estimate with a higher resolution, a more accurate noise floor, and peaks more precise than the Welch method, with low or no spectral leakage. These advantages come at the expense of increased computation and slower tracking.
The spectrum can be expressed in Watts or in decibels. This object can also estimate the maxhold and minhold spectra of the signal.
To estimate the power density spectrum:
Define and set up your spectrum estimator object. See Construction.
Call step
to estimate the power density
spectrum according to the properties of the object.
Starting in R2016b, instead of using the step
method
to perform the operation defined by the System
object™, you can
call the object with arguments, as if it were a function. For example, y
= step(obj,x)
and y = obj(x)
perform
equivalent operations.
SE = dsp.SpectrumEstimator
returns
a System
object, SE
, that computes the frequency
power spectrum or the power density spectrum of real or complex signals.
This System
object uses the Welch’s averaged modified periodogram
method and filter bank based spectral estimation method.
SE = dsp.SpectrumEstimator('
returns
a PropertyName
',PropertyValue
,...)Spectrum Estimator
System
object, SE
,
with each specified property name set to the specified value. You
can specify additional namevalue pair arguments in any order as (Name1,Value1
, ..., NameN,ValueN
).

Sample rate of input Specify the sample rate of the input in, hertz, as a finite
numeric scalar. The default value is 

Spectrum type Specify the spectrum type as one of 

Number of spectral averages Specify the number of spectral averages as a positive, integer
scalar. The Spectrum Estimator computes the current power spectrum
or power density spectrum estimate by averaging the last 

Source of the FFT length value Specify the source of the FFT length value as one of 

FFT Length Specify the length of the FFT that the Spectrum Estimator uses
to compute spectral estimates as a positive, integer scalar. This
property applies when you set the 

Welch or filter bank Specify the spectral estimation method as one of


Number of filter taps per frequency band Specify the number of filter coefficients, or taps, for each
frequency band. This value corresponds to the number of filter coefficients
per polyphase branch. The total number of filter coefficients is given
by 

Window function Specify a window function for the spectral estimator as one
of 

Side lobe attenuation of window Specify the side lobe attenuation of the window as a real, positive
scalar, in decibels (dB). This property applies when you set the 

Frequency range of the spectrum estimate Specify the frequency range of the spectrum estimator as one
of If you set the If you set the If you set the The default value is 

Power units Specify the units used to measure power as one of 

Reference load Specify the load that the spectrum estimator uses as a reference
to compute power values as a real, positive scalar in ohms. The default
value is 

Output maxhold spectrum Set this property to 

Output minhold spectrum Set this property to 
getFrequencyVector  Get the vector of frequencies at which the spectrum is estimated 
getRBW  Get the resolution bandwidth of the spectrum 
reset  Reset the internal states of the spectrum estimator 
step  Estimate the power spectrum or powerdensity spectrum of a signal 
Common to All System Objects  

release  Allow System object property value changes 
[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. Upper Saddle River, NJ: Prentice Hall, 2005
[4] Welch, P. D. “The use of fast Fourier transforms for the estimation of power spectra: A method based on time averaging over short modified periodograms,” IEEE Transactions on Audio and Electroacoustics, Vol. 15, 1967, pp. 70–73.
[5] Harris, F.J. Multirate Signal Processing for Communication Systems. Prentice Hall. 2004, pp. 208–209.