Method ao/noisegen1D


  NOISEGEN1D generates colored noise from white noise.
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  DESCRIPTION: noisegen1D can work in two different modes:
 
  ------------------------------------------------------------------------
  1) Generates colored noise from white noise with a given spectrum. The
  function constructs a coloring filter through a fitting procedure to the
  model provided. If no model is provided an error is prompted. The colored
  noise provided has one-sided psd corresponding to the input model.
 
  This mode correspond to the 'Default' set for the method (see the list of
  parameters).
 
  ALGORITHM:
             1) Fit a set of partial fraction z-domain filters using
                utils.math.psd2tf
             2) Convert to array of MIIR filters
             3) Filter time-series in parallel
 
  CALL:         b = noisegen1D(a, pl)
 
  INPUT:
                - a is a white noise time-series analysis object or a
                vector of analysis objects
                - pl is a plist with the input parameters
 
  OUTPUT:
 
                - b Colored time-series AOs. The coloring filters used
                are stored in the objects procinfo field under the
                parameter 'Filt'.
 
  ------------------------------------------------------------------------
 
  2) Generates noise coloring filters for given input psd models.
 
  This mode correspond to the 'Filter' set for the method (see the list of
  parameters).
 
  ALGORITHM:
             1) Fit a set of partial fraction z-domain filters
             2) Convert to array of MIIR filters
 
  CALL:         fil = noisegen1D(psd, pl)
 
  INPUT:
                - psd is a fsdata analysis object representing the desired
                model for the power spectral density of the colored noise
                - pl is a plist with the input parameters
 
  OUTPUT:
 
                - fil is a filterbank parallel object which elements are
                miir filters. Filters are initialized to avoid startup
                transients.
 
  ------------------------------------------------------------------------
 
  Parameters Description
 
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Method Details
Access public
Defining Class ao
Sealed 0
Static 0

Parameter Description

Default

no description
Key Default Value Options Description
YUNITS '' none Unit on Y axis.
If left empty, it will take the y-units from the input object
MODEL [] none A frequency-series AO describing the model psd
MAXITER 30 none Maximum number of iterations in fit routine.
POLETYPE 3
  • 1
  • 2
  • 3
Choose the pole type for fitting:
  1. use real starting poles
  2. generates complex conjugate poles of the
    type a.*exp(theta*pi*j)
    with theta = linspace(0,pi,N/2+1)
  3. generates complex conjugate poles of the type
    a.*exp(theta*pi*j)
    with theta = linspace(0,pi,N/2+2)
MINORDER 2 none Minimum order to fit with.
MAXORDER 25 none Maximum order to fit with.
WEIGHTS 3 none Choose weighting for the fit:
  1. equal weights for each point
  2. weight with 1/abs(model)
  3. weight with 1/abs(model).^2
  4. weight with inverse of the square mean spread
    of the model
PLOT 0
  • 0
  • 1
Plot results of each fitting step.
DISP 0
  • 0
  • 1
Display the progress of the fitting iteration.
MSEVARTOL 0.01 none Mean Squared Error Variation - Check if the
realtive variation of the mean squared error is
smaller than the value specified. This
option is useful for finding the minimum of Chi-squared.
FITTOL 0.01 none Mean Squared Error Value - Check if the mean
squared error value is lower than the value
specified.
RAND_STREAM [] none Internal MATLAB state of the pseudorandom number generator. You can set the state with a structure which should contain all properties of the MATLAB class RandStream
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Filter

no description
Key Default Value Options Description
FS 1 none The sampling frequency to design for.
IUNITS '' none The input units of the filter.
OUNITS '' none The output units of the filter.
MAXITER 30 none Maximum number of iterations in fit routine.
POLETYPE 3
  • 1
  • 2
  • 3
Choose the pole type for fitting:
  1. use real starting poles
  2. generates complex conjugate poles of the
    type a.*exp(theta*pi*j)
    with theta = linspace(0,pi,N/2+1)
  3. generates complex conjugate poles of the type
    a.*exp(theta*pi*j)
    with theta = linspace(0,pi,N/2+2)
MINORDER 2 none Minimum order to fit with.
MAXORDER 25 none Maximum order to fit with.
WEIGHTS 3 none Choose weighting for the fit:
  1. equal weights for each point
  2. weight with 1/abs(model)
  3. weight with 1/abs(model).^2
  4. weight with inverse of the square mean spread
    of the model
PLOT 0
  • 0
  • 1
Plot results of each fitting step.
DISP 0
  • 0
  • 1
Display the progress of the fitting iteration.
MSEVARTOL 0.01 none Mean Squared Error Variation - Check if the
realtive variation of the mean squared error is
smaller than the value specified. This
option is useful for finding the minimum of Chi-squared.
FITTOL 0.01 none Mean Squared Error Value - Check if the mean
squared error value is lower than the value
specified.
RAND_STREAM [] none Internal MATLAB state of the pseudorandom number generator. You can set the state with a structure which should contain all properties of the MATLAB class RandStream
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Some information of the method ao/noisegen1D are listed below:
Class name ao
Method name noisegen1D
Category Signal Processing
Package name ltpda
VCS Version 175910878ca914560542d679d9d392de37438d84
Min input args 1
Max input args -1
Min output args 1
Max output args -1




©LTP Team