I have a two vectors of spatial data (each about 2000 elements in length). One is a convolved version of the other. I am trying to determine the kernel that would produce such a convolution. I know that I can do this by finding the inverse Fourier transform of the ratio of the Fourier transforms of the output and input vectors. Indeed, when I do this I get more or less the shape I was expecting. However, my kernel vector has the same dimensionality as the two input vectors when in reality the convolution was only using about one fifth (~300-400) of the points. The fact that I am getting the right shape but the wrong number of points makes me think that I am not using the ifft and fft functions quite correctly. It seems like if I were really doing the right thing this should happen naturally. At the moment I am simply doing;
FTInput = fft(in);
FtOutput = fft(out);
kernel = ifft(FtOutput./FTInput).
Is this correct and it's up to me to interpret the output vector correctly or have I oversimplified the task? I'm sure it's the latter, I'm just not sure where.
Answer
If you have noise present in your signal, the straightforward Fourier domain division will cause plenty of errors in your result. Some ways to avoid it are by using the so-called dual channel FFT (Part 1 and Part 2). I can also suggest deconvolution via adaptive filters, LMS or NLMS ([Normalized] Least Mean Squares) filters in particular are easy to understand are not hugely expensive in terms of CPU cycles in case your signals are long. LMS adaptive filters are very robust to noise.
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