# Calculate autocorrelation using FFT in Matlab

I've read some explanations of how autocorrelation can be more efficiently calculated using the fft of a signal, multiplying the real part by the complex conjugate (Fourier domain), then using the inverse fft, but I'm having trouble realizing this in Matlab because at a detailed level.

## Answers

Just like you stated, take the fft and multiply pointwise by its complex conjugate, then use the inverse fft (or in the case of cross-correlation of two signals: Corr(x,y) <=> FFT(x)FFT(y)*)

x = rand(100,1); len = length(x); %# autocorrelation nfft = 2^nextpow2(2*len-1); r = ifft( fft(x,nfft) .* conj(fft(x,nfft)) ); %# rearrange and keep values corresponding to lags: -(len-1):+(len-1) r = [r(end-len+2:end) ; r(1:len)]; %# compare with MATLAB's XCORR output all( (xcorr(x)-r) < 1e-10 )

In fact, if you look at the code of xcorr.m, that's exactly what it's doing (only it has to deal with all the cases of padding, normalizing, vector/matrix input, etc...)

By the Wienerâ€“Khinchin theorem, the power-spectral density (PSD) of a function is the Fourier transform of the autocorrelation. For deterministic signals, the PSD is simply the magnitude-squared of the Fourier transform. See also the convolution theorem.

When it comes to discrete Fourier transforms (i.e. using FFTs), you actually get the cyclic autocorrelation. In order to get proper (linear) autocorrelation, you must zero-pad the original data to twice its original length before taking the Fourier transform. So something like:

x = [ ... ]; x_pad = [x zeros(size(x))]; X = fft(x_pad); X_psd = abs(X).^2; r_xx = ifft(X_psd);