In all cases the cross correlation or autocorrelation computed by xcorr has the

In all cases the cross correlation or autocorrelation

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In all cases, the cross-correlation or autocorrelation computed by xcorr has the 0-th lag in the middle of the sequence, at element or row maxlags+1 (element or row N if maxlags is not specified). Examples The second output, lags , is useful for plotting the cross-correlation or autocorrelation. For example, the estimated autocorrelation of zero-mean Gaussian white noise c ww ( m ) can be displayed for -10 m 10 using ww = randn(1000,1); [c_ww,lags] = xcorr(ww,10,’coeff’); stem(lags,c_ww) Swapping the x and y input arguments reverses (and conjugates) the output correlation sequence. For row vectors, the resulting sequences are reversed left to right; for column vectors, up and down. The following example illustrates this property ( mat2str is used for a compact display of complex numbers). x = [1,2i,3]; y = [4,5,6]; [c1,lags] = xcorr(x,y); c1 = mat2str(c1,2), lags c1 = [12-i*8.9e-016 15-i*8 22-i*10 5-i*12 6+i*8.9e-016] lags = -2 -1 0 1 2 c2 = conj(fliplr(xcorr(y,x))); c2 = mat2str(c2,2) c2 = [12-i*8.9e-016 15-i*8 22-i*10 5-i*12 6+i*8.9e-016] For the case where input argument x is a matrix, the output columns are arranged so that extracting a row and rearranging it into a square array
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xcorr 0-419 produces the cross-correlation matrix corresponding to the lag of the chosen row. For example, the cross-correlation at zero lag can be retrieved by randn(’seed’,0) X = randn(2,2); [M,P] = size(X); c = xcorr(X); c0 = zeros(P); c0(:) = c(M,:) % Extract zero-lag row c0 = 1.7500 0.3079 0.3079 0.1293 You can calculate the matrix of correlation coefficients that the MATLAB function corrcoef generates by substituting c = xcov(X,’coef’) in the last example. The function xcov subtracts the mean and then calls xcorr . Use fftshift to move the second half of the sequence starting at the zeroth lag to the front of the sequence. fftshift swaps the first and second halves of a sequence. Algorithm For more information on estimating covariance and correlation functions, see [1] and [2]. Diagnostics There must be at least one vector input argument; otherwise, xcorr gives the following error message. 1st arg must be a vector or matrix. The string option must be the last argument; otherwise, xcorr gives the following error message. Argument list not in correct order. If the second argument was entered as a scalar, it is taken to be maxlags and no succeeding input can be a scalar. When the second argument is a vector, the first must also be a signal vector. The third argument, when present, must be
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xcorr 0-420 a scalar or a string. If they are not, xcorr gives the appropriate error message(s). 3rd arg is maxlag, 2nd arg cannot be scalar. When b is a vector, a must be a vector. Maxlag must be a scalar. Normally the lengths of the vector inputs should be the same; if they are not, then the only allowable scaling option is ’none’ . If it is not, xcorr gives the following error message. OPTION must be ’none’ for different length vectors A and B. See Also References [1] Bendat, J.S., and A.G. Piersol, Random Data: Analysis and Measurement Procedures , John Wiley & Sons, New York, 1971, p. 332.
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