问题描述
我已经阅读了一些有关如何使用信号的fft，将实部乘以复共轭(傅立叶域)，然后使用逆fft可以更有效地计算自相关的一些解释，但是我在实现时遇到了麻烦这是在Matlab中进行的，因为在详细级别上.
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.
推荐答案
就像您说的那样，取fft并逐点乘以其复共轭，然后使用反fft(或者在两个信号互相关的情况下) :Corr(x,y) <=> FFT(x)FFT(y)*)
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 )
实际上，如果您看一下xcorr.m的代码，这就是它的作用(只是它必须处理填充，规范化，向量/矩阵输入等的所有情况)
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...)
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