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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(...)
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 λ_{min}, the smallest estimated eigenvalue of the signal's correlation matrix. Eigenvalues below the threshold λ_{min}*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 |
---|---|---|
Real-valued | 129 | [0, π] |
Complex-valued | 256 | [0, 2π) |
[S,w] = pmusic(x,p,w) returns the pseudospectrum in the vector S computed at the normalized frequencies specified in vector w, which has two or more elements
[S,w] = pmusic(...,nfft) specifies the integer length of the FFT nfft used to estimate the pseudospectrum. The default value for nfft (entered as an empty vector []) is 256.
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 |
---|---|---|---|
Real-valued | Even | (nfft/2 + 1) | [0, π] |
Real-valued | Odd | (nfft + 1)/2 | [0, π) |
Complex-valued | Even or odd | nfft | [0, 2π) |
[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 | nfft Even/Odd | Range of f |
---|---|---|
Real-valued | Even | [0,fs/2] |
Real-valued | Odd | [0,fs/2) |
Complex-valued | Even or odd | [0,fs) |
[S,f] = pmusic(x,p,f,fs) returns the pseudospectrum in the vector S computed at the frequencies specified in vector f, which has two or more elements
[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 | Windowed Data |
---|---|---|
Data vector | Scalar | Length is nwin |
Data vector | Vector of coefficients | |
Data matrix | Scalar | Data is not windowed. |
Data matrix | 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.
[...] = pmusic(...,freqrange) specifies the range of frequency values to include in f or w. This syntax is useful when x is real. freqrange can be either:
'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.
pmusic(...) with no output arguments plots the pseudospectrum in the current figure window.
[1] Marple, S. Lawrence. Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987, pp. 373–378.
[2] Schmidt, R. O. "Multiple Emitter Location and Signal Parameter Estimation." IEEE^{®} Transactions on Antennas and Propagation. Vol. AP-34, March, 1986, pp. 276–280.
[3] Stoica, Petre, and Randolph L. Moses. Spectral Analysis of Signals. Upper Saddle River, NJ: Prentice Hall, 2005.