The FFT is the main numerical engine behind the conventional spectrum analyser and many automated measurements in Visual Analyser. It transforms a finite block of samples from the time domain into uniformly spaced frequency components.
Frequency grid and resolution
For a sampling rate Fs and an FFT length N, adjacent bins are separated by Fs/N. Increasing N improves frequency spacing but also increases acquisition time and latency.
Window functions
Hann, Hamming, Blackman and other windows reduce discontinuities at the edges of the block. Each window trades main-lobe width, side-lobe rejection and amplitude accuracy differently, so the best choice depends on whether the goal is separating close tones, reading amplitude or detecting weak components near a strong one.
FFT overlap: 0%, 50% and 75%
Without overlap, every FFT uses a completely new block. At 50% overlap, half of the previous samples are reused; at 75%, only one quarter of the block is new. Overlap increases the update rate, produces a smoother time evolution and avoids under-representing events that occur near window edges. It does not change the nominal bin spacing, but it increases computational load.
0%
Lowest processing load and independent blocks.
50%
Good general compromise for continuous analysis.
75%
More frequent updates and denser time tracking.
Choose deliberately
Overlap is most useful with windowed, continuously acquired signals.
Beyond the FFT
VA also provides Custom Spectrum analysis based on Goertzel filters when the required frequencies are arbitrary rather than uniformly spaced.