pmusic Computes Psuedospectrum using MUSIC algorithm [S,w] = pmusic(x,p) [S,w] = pmusic(x,p,w) [S,w] = pmusic(x,p,nfft) [S,w] = pmusic(x,p,nfft,fs) [S,w] = pmusic(x,p,f,fs) [S,f] = pmusic(...,'corr') [S,f] = pmusic(x,p,nfft,fs,nwin,noverlap) [...] = pmusic(...,freqrange) [...,v,e] = pmusic(...) x: int|double - vector|matrix Input signal. In case of a matrix, each row of x represents a seperate observation of the signal. If 'corr' flag is specified, then x is the correlation matrix. If w is not specified in the input, it is determined by the algorithm. If x is real valued, then range of w is [0, pi]. Otherwise, the range of w is [0, 2pi) p: int|double - scalar|vector p(1) is the dimension of the signal subspace p(2), if specified, represents a threshold that is multiplied by the smallest estimated eigenvalue of the signal's correlation matrix. w: int|double - vector w is the vector of normalized frequencies over which the pseuspectrogram is to be computed. nfft: int - scalar (Default = 256) Length of the fft used to compute pseudospectrum. The length of S (and hence w/f) depends on the type of values in x and nfft. If x is real, length of s is (nfft/2 + 1) {Range of w = [0, pi]} if nfft is even and (nfft+1)/2 {Range of w = [0, pi)} otherwise. If x is complex, length of s is nfft. fs: int|double - scalar (Default = 1) Sampling rate. Used to convert the normalized frequencies (w) to actual values (f) and vice-versa. nwin: int|double - scalar (int only)|vector (Default = 2*p(1)) If nwin is scalar, it is the length of the rectangular window. Otherwise, the vector input is considered as the window coefficients. Not used if 'corr' flag present. noverlap: int - scalar (Default = nwin-1) number of points by which successive windows overlap. noverlap not used if x is a matrix freqrange: string The range of frequencies over which the pseudospetrogram is computed. Three possible values - 'onesided', 'twosided', 'centered' 'corr' flag Presence indicates that the primary input x is actually a correlation matrix [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 pburg| peig | periodogram | pmtm | prony | pwelch | rooteig | rootmusic Ayush Baid References [1] Petre Stoica and Randolph Moses, Introduction To Spectral Analysis, Prentice-Hall, 1997, pg. 15 [2] S. J. Orfanidis, Optimum Signal Processing. An Introduction. 2nd Ed., Macmillan, 1988.