Pseudospectrum using MUSIC algorithm
[S,w] = pmusic(x,p)
[S,w] = pmusic(x,p,w)
[S,w] = pmusic(...,nfft)
[S,f] = pmusic(x,p,nfft,fs)
[S,f] = pmusic(x,p,f,fs)
[S,f] = pmusic(...,'corr')
[S,f] = pmusic(x,p,nfft,fs,nwin,noverlap)
[...] = pmusic(...,freqrange)
[...,v,e] = pmusic(...)
[S,w] = pmusic(x,p) implements the MUSIC (Multiple Signal Classification) algorithm and returns S, the pseudospectrum estimate of the input signal x, and a vector w of normalized frequencies (in rad/sample) at which the pseudospectrum is evaluated. The pseudospectrum is calculated using estimates of the eigenvectors of a correlation matrix associated with the input data x, where x is specified as either:
A row or column vector representing one observation of the signal
A rectangular array for which each row of x represents a separate observation of the signal (for example, each row is one output of an array of sensors, as in array processing), such that x'*x is an estimate of the correlation matrix
Note You can use the output of corrmtx to generate such an array x.
You can specify the second input argument p as either:
A scalar integer. In this case, the signal subspace dimension is p.
A two-element vector. In this case, p(2), the second element of p, represents a threshold that is multiplied by λmin, the smallest estimated eigenvalue of the signal's correlation matrix. Eigenvalues below the threshold λmin*p(2) are assigned to the noise subspace. In this case, p(1) specifies the maximum dimension of the signal subspace.
Note: If the inputs to pmusic are real sinusoids, set the value of p to double the number of input signals. If the inputs are complex sinusoids, set p equal to the number of inputs.
The extra threshold parameter in the second entry in p provides you more flexibility and control in assigning the noise and signal subspaces.
S and w have the same length. In general, the length of the FFT and the values of the input x determine the length of the computed S and the range of the corresponding normalized frequencies. The following table indicates the length of S (and w) and the range of the corresponding normalized frequencies for this syntax.
S Characteristics for an FFT Length of 256 (Default)
|Real/Complex Input Data||Length of S and w||Range of the Corresponding Normalized Frequencies|
The following table indicates the length of S and w, and the frequency range for w in this syntax.
S and Frequency Vector Characteristics
|Real/Complex Input Data||nfft Even/Odd||Length of S and w||Range of w|
(nfft/2 + 1)
(nfft + 1)/2
Even or odd
[S,f] = pmusic(x,p,nfft,fs) returns the pseudospectrum in the vector S evaluated at the corresponding vector of frequencies f (in Hz). You supply the sampling frequency fs in Hz. If you specify fs with the empty vector , the sampling frequency defaults to 1 Hz.
The frequency range for f depends on nfft, fs, and the values of the input x. The length of S (and f) is the same as in the S and Frequency Vector Characteristics above. The following table indicates the frequency range for f for this syntax.
S and Frequency Vector Characteristics with fs Specified
Real/Complex Input Data
Range of f
Even or odd
[S,f] = pmusic(...,'corr') forces the input argument x to be interpreted as a correlation matrix rather than matrix of signal data. For this syntax x must be a square matrix, and all of its eigenvalues must be nonnegative.
[S,f] = pmusic(x,p,nfft,fs,nwin,noverlap) allows you to specify nwin, a scalar integer indicating a rectangular window length, or a real-valued vector specifying window coefficients. Use the scalar integer noverlap in conjunction with nwin to specify the number of input sample points by which successive windows overlap. noverlap is not used if x is a matrix. The default value for nwin is 2*p(1) and noverlap is nwin-1.
With this syntax, the input data x is segmented and windowed before the matrix used to estimate the correlation matrix eigenvalues is formulated. The segmentation of the data depends on nwin, noverlap, and the form of x. Comments on the resulting windowed segments are described in the following table.
Windowed Data Depending on x and nwin
Input data x
Form of nwin
Length is nwin
Vector of coefficients
Data is not windowed.
Vector of coefficients
length(nwin) must be the same as the column length of x, and noverlap is not used.
See the Eigenvector Length Depending on Input Data and Syntax below for related information on this syntax.
'onesided' — returns the one-sided PSD of a real input signal, x. If nfft is even, Pxx has lengthnfft/2+1 and is computed over the interval [0,π]. If nfft is odd, the length of Pxx is (nfft+1)/2 and the frequency interval is [0,π). When your specify fs , the intervals are [0,fs/2) and [0,fs/2] for even and odd lengthnfftrespectively.
'twosided' — returns the two-sided PSD for either real or complex input, x. In this case, Pxx has length nfft and is computed over the interval [0,2π). When you specify fs, the frequency interval is [0,fs).
'centered' — returns the centered two-sided PSD for either real or complex input, x. In this case, Pxx has length nfft and is computed over the interval (-π, π] for even length nfft and (-π, π]) for odd length nfft. When you specify fs, the frequency intervals are (-fs/2, fs/2] and (-fs/2,fs/2) for even and odd length nfft respectively.
Note You can put the string arguments freqrange or 'corr' anywhere in the input argument list after p.
[...,v,e] = pmusic(...) returns the matrix v of noise eigenvectors, along with the associated eigenvalues in the vector e. The columns of v span the noise subspace of dimension size(v,2). The dimension of the signal subspace is size(v,1)-size(v,2). For this syntax, e is a vector of estimated eigenvalues of the correlation matrix.
This example analyzes a signal vector, x, assuming that two real sinusoidal components are present in the signal subspace. In this case, the dimension of the signal subspace is 4, because each real sinusoid is the sum of two complex exponentials.
n = 0:199; x = cos(0.257*pi*n) + sin(0.2*pi*n) + 0.01*randn(size(n)); pmusic(x,4) % Set p to 4 because there are two real inputs
This example analyzes the same signal vector, x, with an eigenvalue cutoff of 10% above the minimum. Setting p(1) = Inf forces the signal/noise subspace decision to be based on the threshold parameter, p(2). Specify the eigenvectors of length 7 using the nwin argument, and set the sampling frequency, fs, to 8 kHz:
rng default n = 0:199; x = cos(0.257*pi*n) + sin(0.2*pi*n) + 0.01*randn(size(n)); [P,f] = pmusic(x,[Inf,1.1],,8000,7); % Window length = 7 plot(f,20*log10(abs(P))) xlabel 'Frequency (Hz)', ylabel 'Power (dB)' title 'Pseudospectrum Estimate via MUSIC', grid on
Supply a positive definite correlation matrix, R, for estimating the spectral density. Use the default 256 samples.
R = toeplitz(cos(0.1*pi*(0:6))) + 0.1*eye(7); pmusic(R,4,'corr');
Enter a signal data matrix, Xm, generated from data using corrmtx.
n = 0:699; x = cos(0.257*pi*(n)) + 0.1*randn(size(n)); Xm = corrmtx(x,7,'mod'); pmusic(Xm,2);
Use the same signal, but let pmusic form the 100-by-7 data matrix using its windowing input arguments. In addition, specify an FFT of length 512.
n = 0:699; x = cos(0.257*pi*(n)) + 0.1*randn(size(n)); [PP,ff] = pmusic(x,2,512,,7,0); pmusic(x,2,512,,7,0)
In the process of estimating the pseudospectrum, pmusic computes the noise and signal subspaces from the estimated eigenvectors vj and eigenvalues λj of the signal's correlation matrix. The smallest of these eigenvalues is used in conjunction with the threshold parameter p(2) to affect the dimension of the noise subspace in some cases.
The length n of the eigenvectors computed by pmusic is the sum of the dimensions of the signal and noise subspaces. This eigenvector length depends on your input (signal data or correlation matrix) and the syntax you use.
The following table summarizes the dependency of the eigenvector length on the input argument.
Eigenvector Length Depending on Input Data and Syntax
Form of Input Data x
Comments on the Syntax
Length n of Eigenvectors
Row or column vector
nwin is specified as a scalar integer.
Row or column vector
nwin is specified as a vector.
Row or column vector
nwin is not specified.
If nwin is specified as a scalar, it is not used. If nwin is specified as a vector, length(nwin) must equal m.
m-by-m nonnegative definite matrix
The string 'corr' is specified and nwin is not used.
You should specify nwin > p(1) or length(nwin) > p(1) if you want p(2) > 1 to have any effect.
The name MUSIC is an acronym for MUltiple SIgnal Classification. The MUSIC algorithm estimates the pseudospectrum from a signal or a correlation matrix using Schmidt's eigenspace analysis method . The algorithm performs eigenspace analysis of the signal's correlation matrix in order to estimate the signal's frequency content. This algorithm is particularly suitable for signals that are the sum of sinusoids with additive white Gaussian noise. The eigenvalues and eigenvectors of the signal's correlation matrix are estimated if you don't supply the correlation matrix.
The MUSIC pseudospectrum estimate is given by
where N is the dimension of the eigenvectors and vk is the k-th eigenvector of the correlation matrix. The integer p is the dimension of the signal subspace, so the eigenvectors vk used in the sum correspond to the smallest eigenvalues and also span the noise subspace. The vector e(f) consists of complex exponentials, so the inner product
amounts to a Fourier transform. This is used for computation of the pseudospectrum estimate. The FFT is computed for each vk and then the squared magnitudes are summed.
 Marple, S. Lawrence. Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987, pp. 373–378.
 Schmidt, R. O. "Multiple Emitter Location and Signal Parameter Estimation." IEEE® Transactions on Antennas and Propagation. Vol. AP-34, March, 1986, pp. 276–280.
 Stoica, Petre, and Randolph L. Moses. Spectral Analysis of Signals. Upper Saddle River, NJ: Prentice Hall, 2005.