The FFTFilter applies a short-term Fast Fourier Transformation (FFT) to selected channels of its input signal, resulting in a time series of frequency spectra. The computed spectra may be displayed in visualization windows.
Typically, the FFTFilter is used for spectral estimation and demodulation, instead of the ARFilter.
Depending on configuration, the FFT filter's output signal will be the computed spectrum, or the unchanged input. Possible values are
- 0 for input connect-through--
this option allows using the FFTFilter for visualization purposes.
- 1 for power spectrum--
as with the ARFilter, the output signal's elements will correspond to frequency bins.
- 2 for complex amplitudes--
the output will be complex fourier coefficients in halfcomplex format, with the spectrum's imaginary part appended to the real part.
A list of input channels for which the FFT is computed. When FFTOutputSignal is set to other than 0, FFTInputChannels list entries determine the correspondence between input and output channels.
The length of the input data window over which the FFT is computed, given as a time value in seconds, or the number of signal blocks as in the following examples:
1.34s 500ms 5
The FFT will be computed once per data block. If the length of the input data window exceeds that of a data block, FFT windows will overlap. If the data window is shorter than a data block, only the most recent samples will enter into the FFT.
Selects the type of sidelobe suppression window. Possible values are
- 1 for a Hamming window,
- 2 for a Hann window,
- 3 for a Blackman window.
A nonzero value selects visualization of the FFT-computed power spectrum. Independently of the FFTOutputSignal parameter's value, it is always the power spectrum that is visualized.