In terms of signal analysis (especially frequency decomposition) would it be bad to use cyclic autocorrelation (AC) instead of acyclic/linear AC?
My use case:
I'm trying to characterize events in a sensor stream by periodicity/frequency. At the moment, I'm using DFT spectrum (spectral density) and AC of sliding windows.
On the one handside it's algorithmically way cheaper to calculate the cyclic AC via FFT than acyclic (pad with zeroes and do double length FFT). However, the cyclic AC looks similar (though not equal) to the acyclic for the first N/2 lags and is self-symmetric - thus at most half the information of acyclic AC can be contained.
If the cyclic AC would be sufficient for detection, that would mean better performance compared to linear AC (in terms of memory usage and computation time). I then could also calculate AC inplace (better parallelization potential).
Is there any rule when cyclic AC should NOT be used?
Or perhaps, are there known cases, when cyclic AC might actually be better than linear AC?
Thanks in advance :-)