FFT process
The FFT process mathematically converts the standard time-domain signal (repetitive or single-shot acquisition) into its
frequency components.
The FFT function processes the waveform record and displays the FFT frequency domain record, which contains the input signal
frequency components from DC (0 Hz) to ½ the sample rate (also called the Nyquist frequency).
Nyquist frequency
The highest frequency that any digital oscilloscope can measure without errors is one-half of the sample rate. This frequency is
called the Nyquist frequency.
The FFT waveform displays the input signal frequency components from DC (0 Hz) to the Nyquist frequency.
FFT and aliasing
Aliasing occurs when the input frequency of a signal is greater than one half of the sampling frequency (the sample rate).
Set the sample rate high enough so that the signals in the spectrum appear at their correct frequency as opposed to a lower
aliased frequency value. Also, complex signal shapes that have many harmonics in them, such as a triangle or square wave, can
appear to be OK in the time domain when in fact many of the harmonics in that signal are aliased.
One way to check for aliasing is to increase the sample rate and observe whether any of the harmonics unwrap to different
frequency locations.
Another way to recognize aliasing is to realize that higher order harmonics usually have decreasing magnitudes compared to
lower order harmonics. Thus, if you see a series of increasing harmonic magnitude values as frequency increases then you can
suspect that they may be aliased. In the spectral math waveform, the actual higher frequency components are under sampled,
and therefore they appear as lower frequency aliases that "fold back" around the Nyquist point. (See the next figure.) You may
test by increasing the sample rate and observing if aliases unwrap to different frequency positions.
Measurement concepts
438 MSO54, MSO56, MSO58, MSO58LP, MSO64 Help
To Next Page
Loading...
Need help?
General
