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Owning Class: spectral analysis
Requires: MathScript RT Module
[w, pw] = root_music(x, order)
[w, pw] = root_music(x, order, 'corr')
[f, pw] = root_music(x, order, fs)
[f, pw] = root_music(x, order, fs, 'corr')
Legacy Name: rootmusic
Uses the multiple signal classification (MUSIC) algorithm to calculate the frequency and power components of the input signal.
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 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). |
fs | Specifies the sampling frequency of the input sequence in Hz. If you specify fs, LabVIEW returns the output frequencies in Hz. Otherwise, LabVIEW returns the output frequencies in radians per sample. |
'corr' | Specifies that x is a square autocorrelation matrix. |
Name | Description |
---|---|
w | Returns the estimated radius frequencies of the sinusoids. w ranges from 0 to pi. |
f | Returns the estimated frequencies of the sinusoids. f ranges from 0 to fs / 2. |
pw | Returns the estimated powers of the estimated sinusoids. |
The following table lists the support characteristics of this function.
Supported in the LabVIEW Run-Time Engine | Yes |
Supported on RT targets | Yes |
Suitable for bounded execution times on RT | Not characterized |
fs = 1000;
t = 0:1/fs:0.2;
noise = randnormal(size(t));
x = sin(2*pi*100*t) + cos(2*pi*250*t) + noise;
[f, pw] = root_music(x, 4, fs);