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WHITEN2D whiten the noise for two cross correlated time series. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DESCRIPTION: whiten2D whitens cross-correlated time-series. Whitening filters are constructed by a fitting procedure to the cross-spectrum models provided. Note: The function assumes that the input model corresponds to the one-sided csd of the data to be whitened. ALGORITHM: 1) Fit a set of partial fraction z-domain filters using utils.math.psd2wf 2) Convert to bank 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 = whiten2D(a, pl) % returns whitened time-series AOs [b1,b2] = whiten2D(a1, a2, pl) [b1,b2,...,bn] = whiten2D(a1,a2,...,an, pl); Note: Input AOs must come in couples. Note: this method cannot be used as a modifier, the call a.whiten2D(pl) is forbidden. INPUT: - a is a couple of two colored noise time-series AOs OUTPUT: - b is a couple of "whitened" time-series AOs. The whitening filters used are stored in the objects procinfo field under the parameters: - b(1): 'Filt11' and 'Filt12' - b(2): 'Filt21' and 'Filt22' 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 |
Default |
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| no description | |||
| Key | Default Value | Options | Description |
| whiten2D | |||
| 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 |
| 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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| KEEPVAR | 0 |
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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 |
Example |
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| plist('CSD11', [[]], 'CSD12', [[]], 'CSD21', [[]], 'CSD22', [[]], 'MAXITER', [30], 'POLETYPE', [3], 'MINORDER', [2], 'MAXORDER', [25], 'WEIGHTS', [3], 'PLOT', [false], 'DISP', [false], 'MSEVARTOL', [0.01], 'FITTOL', [0.01], 'USESYM', [0], 'KEEPVAR', [false], 'RAND_STREAM', [[]]) |
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| Some information of the method ao/whiten2D are listed below: | |
|---|---|
| Class name | ao |
| Method name | whiten2D |
| Category | Signal Processing |
| Package name | ltpda |
| Can be used as modifier | 0 |
| Supported numeric types | {'double'} |
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Method: ao/whiten1D | Method: ao/window | ![]() |
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