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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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Method Details | |
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Access | public |
Defining Class | ao |
Sealed | 0 |
Static | 0 |
Sets for this method … |
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Default |
Filter |
Default |
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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 |
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Choose the pole type for fitting:
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MINORDER | 2 | none | Minimum order to fit with. |
MAXORDER | 25 | none | Maximum order to fit with. |
WEIGHTS | 3 | none | Choose weighting for the fit:
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PLOT | 0 |
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Plot results of each fitting step. |
DISP | 0 |
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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 |
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Use symbolic calculation in eigen-decomposition.
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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 |
Filter |
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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 |
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Choose the pole type for fitting:
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MINORDER | 2 | none | Minimum order to fit with. |
MAXORDER | 25 | none | Maximum order to fit with. |
WEIGHTS | 3 | none | Choose weighting for the fit:
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PLOT | 0 |
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Plot results of each fitting step. |
DISP | 0 |
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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 |
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Use symbolic calculation in eigen-decomposition.
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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 |
Some information of the method ao/noisegen2D are listed below: | |
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Class name | ao |
Method name | noisegen2D |
Category | Signal Processing |
Package name | ltpda |
VCS Version | 175910878ca914560542d679d9d392de37438d84 |
Min input args | 2 |
Max input args | -1 |
Min output args | 1 |
Max output args | -1 |
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Method: ao/noisegen1D | Method: ao/normdist | ![]() |
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