|LabVIEW 2018 Sound and Vibration Toolkit Help|
|LabVIEW 2019 Sound and Vibration Toolkit Help|
The Fast Fourier Transform (FFT) resolves a time waveform into its sinusoidal components. The FFT takes a block of time-domain data and returns the frequency spectrum of the data. The FFT is a digital implementation of the Fourier transform. Thus, the FFT does not yield a continuous spectrum. Instead, the FFT returns a discrete spectrum, in which the frequency content of the waveform is resolved into a finite number of frequency lines, or bins.
The algorithm used to transform samples of the data from the time domain into the frequency domain is the discrete Fourier transform (DFT). The DFT establishes the relationship between the samples of a signal in the time domain and their representation in the frequency domain. The DFT is widely used in the fields of spectral analysis, applied mechanics, acoustics, medical imaging, numerical analysis, instrumentation, and telecommunications.
In some applications, you need to obtain spectral information with a very fine frequency resolution over a limited portion of the baseband span. In other words, you must zoom in on a spectral region to observe the details of that spectral region. Use the zoom Fast Fourier Transform (FFT) to obtain spectral information over a limited portion of the baseband span and with greater resolution. Just as in baseband analysis, the acquisition time determines the frequency resolution of the computed spectrum. The number of samples used in the transform determines the number of lines computed in the spectrum.
|Note For simplicity, the remainder of this document uses the term FFT to denote both the FFT and the DFT.|