I've been experimenting with a few different techniques that I can find for a freq shifting (specifically I want to shift high freq signals to a lower freq). At the moment I'm trying to use this technique -
take the original signal, x(t), multiply it by: cos(2 PI dF t), sin(2 PI dF t) R(t) = x(t) cos(2 PI dF t) I(t) = x(t) sin(2 PI dF t) where dF is the delta frequency to be shifted. Now you have two time series signals: R(t) and I(t). Conduct complex Fourier transform using R(t) as real and I(t) as imaginary parts. The results will be frequency shifted spectrum.
I have interpreted this into the following code -
for(j=0;j<(BUFFERSIZE/2);j++)
{
Partfunc = (((double)j)/2048);
PreFFTShift[j+x] = PingData[j]*(cos(2*M_PI*Shift*(Partfunc)));
PreFFTShift[j+1+x] = PingData[j]*(sin(2*M_PI*Shift*(Partfunc)));
x++;
}
//INITIALIZE FFT
status = arm_cfft_radix4_init_f32(&S, fftSize, ifftFlag, doBitReverse);
//FFT on FFTData
arm_cfft_radix4_f32(&S, PreFFTShift);
This builds me an array with interleaved real and imag data and then FFT. I then inverse the FFT, but the output im getting is pretty garbled. Results seem huge in comparison to what I think they should be, and although there are a few traces of a freq shifted signal, its hard to tell as the result seems mostly pretty noisy.
I've also attempted simply revolving the array values of a standard FFT of my original signal to get a freq shift, but to no avail. Is there a better method for doing this?