Method ao/noisegen2D


  NOISEGEN2D generates cross correleted colored noise from white noise.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
  DESCRIPTION: noisegen2D can work in two different modes:
  
  ------------------------------------------------------------------------
  
  1) Generates colored noise from white noise with a given cross spectrum.
      This mode correspond to the 'Default' set for the method (see the
      list of parameters).
  
    The coloring filter is constructed by a fitting procedure to the models
    provided. If no model is provided an error is prompted. The
    cross-spectral matrix is assumed to be frequency by frequency of the
    type: 
  
                          / csd11(f)  csd12(f) \
                CSD(f) =  |                    |
                          \ csd21(f)  csd22(f) /
  
               Note: The function output colored noise data with one-sided
               csd corresponding to the model provided.
 
  ALGORITHM:
             1) Fit a set of partial fraction z-domain filters
             2) Convert to array of MIIR filters
             3) Filter time-series in parallel
                The filtering process is:
                b(1) = Filt11(a(1)) + Filt12(a(2))
                b(2) = Filt21(a(1)) + Filt22(a(2))
  
  
  CALL:             b = noisegen2D(a, pl) % returns colored time-series AOs
                    b = noisegen2D(a, pl)
                    [b1,b2] = noisegen2D(a1, a2, pl)
                    [b1,b2,...,bn] = noisegen2D(a1,a2,...,an, pl);
                    Note: this method cannot be used as a modifier, the
                    call a.noisegen2D(pl) is forbidden
  
  INPUT:
  
                - a is at least a couple of time series analysis objects
                - pl is a parameter list, see the list of accepted
                parameters below
  
  OUTPUT:
  
                - b are a couple of colored time-series AOs. The coloring
                filters used are stored in the objects procinfo field under
                the parameters:
                  - b(1): 'Filt11' and 'Filt12'
                  - b(2): 'Filt21' and 'Filt22'
  ------------------------------------------------------------------------
  
  2) Generates coloring filter
      This mode correspond to the 'Filter' set for the method (see the
      list of parameters).
  
    The coloring filter is constructed by a fitting procedure to the models
    provided. The cross-spectral matrix is assumed to be frequency by
    frequency of the type: 
  
                          / csd11(f)  csd12(f) \
                CSD(f) =  |                    |
                          \ csd21(f)  csd22(f) /
  
  ALGORITHM:
             1) Fit a set of partial fraction z-domain filters
             2) Convert to array of MIIR filters
  
 
  CALL:             fil = noisegen2D(csd11,csd12,csd21,csd22, pl)
                    fil = noisegen2D(csd11,csd12,csd22, pl)
                    Note: this method cannot be used as a modifier, the
                    call a.noisegen2D(pl) is forbidden
  
  INPUT:
  
                - csd11, csd12, csd21,csd22 are the terms of the
                cross-spectral matrix. They must be frequency series
                analysis objects.
                - pl is a parameter list, see the list of accepted
                parameters below
  
  OUTPUT:
  
                - fil is a matrix object which represent a two dimensional
                filter. The elements of fil are filterbanks parallel
                objects of miir filters. Filters are initialized to
                avoid startup transients.
  
  ------------------------------------------------------------------------
 
  Parameters Description
 
  VERSION:     $Id: noisegen2D.m,v 1.35 2012/02/09 14:33:54 ingo Exp $
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Method Details
Access public
Defining Class ao
Sealed 0
Static 0

Parameter Description

Default

no description
Key Default Value Options Description
CSD11 [] none A frequency-series AO describing the model csd11
CSD12 [] none A frequency-series AO describing the model csd12
CSD21 [] none A frequency-series AO describing the model csd21
CSD22 [] none A frequency-series AO describing the model csd22
YUNITS {'', ''} none Unit on Y axis.
If left empty, it will take the y-units from the input objects
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.
USESYM 0
  • 0
  • 1
  • 2
Use symbolic calculation in eigen-decomposition.
  • 0 - perform double-precision calculation in the
    eigendecomposition procedure to identify 2-Dim
    systems and for poles stabilization
  • 1 - uses symbolic math toolbox variable precision
    arithmetic in the eigen-decomposition for 2-Dim
    system identification and double-precison for
    poles stabilization
  • 2 - uses symbolic math toolbox variable precision
    arithmetic in the eigen-decomposition for 2-Dim
    system identification and for poles stabilization.
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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.
USESYM 0
  • 0
  • 1
  • 2
Use symbolic calculation in eigen-decomposition.
  • 0 - perform double-precision calculation in the
    eigendecomposition procedure to identify 2-Dim
    systems and for poles stabilization
  • 1 - uses symbolic math toolbox variable precision
    arithmetic in the eigen-decomposition for 2-Dim
    system identification and double-precison for
    poles stabilization
  • 2 - uses symbolic math toolbox variable precision
    arithmetic in the eigen-decomposition for 2-Dim
    system identification and for poles stabilization.
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Some information of the method ao/noisegen2D are listed below:
Class name ao
Method name noisegen2D
Category Signal Processing
CVS Version $Id: noisegen2D.m,v 1.35 2012/02/09 14:33:54 ingo Exp $
Min input args 2
Max input args -1
Min output args 1
Max output args -1




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