Owning Class: spectral analysis
Requires: MathScript RT Module
pspec_music(x, order)
pspec_music(x, order, fftsize)
pspec_music(x, order, range)
pspec_music(x, order, 'corr')
pspec_music(x, order, fftsize, fs)
pspec_music(x, order, fftsize, range)
pspec_music(x, order, fftsize, 'corr')
pspec_music(x, order, 'corr', range)
pspec_music(x, order, fftsize, fs, 'corr')
pspec_music(x, order, fftsize, fs, range)
pspec_music(x, order, fftsize, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap)
pspec_music(x, order, fftsize, fs, win, 'corr')
pspec_music(x, order, fftsize, fs, win, range)
pspec_music(x, order, fftsize, fs, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
pspec_music(x, order, fftsize, fs, win, noverlap, range)
pspec_music(x, order, fftsize, fs, win, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
S = pspec_music(x, order)
S = pspec_music(x, order, fftsize)
S = pspec_music(x, order, range)
S = pspec_music(x, order, 'corr')
S = pspec_music(x, order, fftsize, fs)
S = pspec_music(x, order, fftsize, range)
S = pspec_music(x, order, fftsize, 'corr')
S = pspec_music(x, order, 'corr', range)
S = pspec_music(x, order, fftsize, fs, 'corr')
S = pspec_music(x, order, fftsize, fs, range)
S = pspec_music(x, order, fftsize, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap)
S = pspec_music(x, order, fftsize, fs, win, 'corr')
S = pspec_music(x, order, fftsize, fs, win, range)
S = pspec_music(x, order, fftsize, fs, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
S = pspec_music(x, order, fftsize, fs, win, noverlap, range)
S = pspec_music(x, order, fftsize, fs, win, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w] = pspec_music(x, order)
[S, w] = pspec_music(x, order, fftsize)
[S, w] = pspec_music(x, order, range)
[S, w] = pspec_music(x, order, 'corr')
[S, w] = pspec_music(x, order, fftsize, range)
[S, w] = pspec_music(x, order, fftsize, 'corr')
[S, w] = pspec_music(x, order, 'corr', range)
[S, w] = pspec_music(x, order, fftsize, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs)
[S, f] = pspec_music(x, order, fftsize, fs, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap)
[S, f] = pspec_music(x, order, fftsize, fs, win, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, win, range)
[S, f] = pspec_music(x, order, fftsize, fs, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, range)
[S, f] = pspec_music(x, order, fftsize, fs, win, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w, evec, eval] = pspec_music(x, order)
[S, w, evec, eval] = pspec_music(x, order, fftsize)
[S, w, evec, eval] = pspec_music(x, order, range)
[S, w, evec, eval] = pspec_music(x, order, 'corr')
[S, w, evec, eval] = pspec_music(x, order, fftsize, range)
[S, w, evec, eval] = pspec_music(x, order, fftsize, 'corr')
[S, w, evec, eval] = pspec_music(x, order, 'corr', range)
[S, w, evec, eval] = pspec_music(x, order, fftsize, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
Legacy Name: pmusic
Uses the multiple signal classification (MUSIC) algorithm to calculate the pseudospectrum of an input signal. If you do not request an output, this function plots the pseudospectrum in the current plot window.
| Name | Description | ||||
|---|---|---|---|---|---|
| x | Specifies the input signal. x can be a real or complex vector or matrix. If you specify 'corr', x is a square autocorrelation matrix. Otherwise, x is a real matrix whose elements are stacked row-wise. | ||||
| order | Specifies the dimension information of the signal subspace. order can be a scalar or a vector of two elements. If order is a scalar, order specifies the dimension of the signal subspace. If order is a vector of two elements, order(2) separates the signal and noise subspace. All eigenvalues greater than order(2)*the smallest eigenvalue belong to the signal subspace, and the signal subspace dimension is less than or equal to order(1). | ||||
| fftsize | Specifies the length of the FFT to perform on the estimated eigenvectors of the noise subspace. The default is 256. | ||||
| fs | Specifies the sampling frequency of the input sequence in Hz. | ||||
| win | Specifies the window to apply to each row of the data matrix. win can be an integer or a vector. If win is an integer, LabVIEW applies a Hamming window with a length of win. If win is a vector, win represents the window whose length equals the number of columns in the data matrix. The default is 2*order - 1. | ||||
| noverlap | Specifies the number of points that overlap when LabVIEW creates the data matrix. The default is 0. | ||||
| 'corr' | Specifies that x is a square autocorrelation matrix. | ||||
| range | Specifies the range of the estimated pseudospectrum. range is a string that accepts the following values.
|
| Name | Description |
|---|---|
| S | Returns the estimated pseudospectrum of the input signal. If x is real and range is 'half', the length of S is fftsize / 2 + 1 for an even fftsize and (fftsize + 1) / 2 for an odd fftsize. Otherwise, the length of S is fftsize. |
| w | Returns the discrete frequencies in radians per sample at the points where LabVIEW evaluates S. |
| f | Returns the frequencies in Hz at the points where LabVIEW evaluates S. |
| evec | Returns the matrix whose columns are noise subspace eigenvectors. |
| eval | Returns all the eigenvalues of the autocorrelation matrix of the input data. |
This function is supported in the LabVIEW Run-Time Engine only if you request an output from the function. This function can remain in your scripts when you build a stand-alone application or shared library, but if you do not request an output, the LabVIEW Run-Time Engine does not execute this function. If you request an output, the LabVIEW Run-Time Engine executes this function as normal.
fs = 1000;
t = 0:1/fs:0.2;
fftsize = 1024;
noise = randnormal(size(t));
x = sin(2*pi*100*t) + cos(2*pi*250*t) + noise;
S = pspec_music(x, 4, fftsize, fs);
psd_burg
psd_covar
psd_mcovar
psd_periodogram
psd_welch
psd_yule
pspec_eign