FFT Frequency Resolution Calculator

Sample Rate (Hz)

Sample Count (N)

Record Duration (s)

FFT Points (Optional)

Zero Pad Factor

Window Type

Target Frequency (Hz)

Display Span Around Target (Hz)

Use sample count or duration. If both are entered, duration updates the effective sample count from the chosen sample rate.

Example Data Table

Sample Rate (Hz)	Samples	Record Length (s)	True Resolution (Hz)	FFT Points	Displayed Bin Spacing (Hz)
1000	1024	1.024	0.976563	1024	0.976563
1000	1024	1.024	0.976563	4096	0.244141
5000	2048	0.4096	2.441406	2048	2.441406
10000	8192	0.8192	1.220703	16384	0.610352

Formula Used

Δf = f_s / N = 1 / T

True FFT frequency resolution depends on total record length. Here, f_s is the sample rate, N is the number of acquired samples, and T is the observation time.

Displayed Bin Spacing = f_s / N_FFT

Zero padding changes displayed bin spacing by increasing N_FFT, but it does not improve the actual ability to separate two close frequencies. The calculator also estimates window ENBW and main lobe width for better interpretation.

How to Use This Calculator

Enter the sample rate in hertz.
Provide sample count or record duration.
Choose an FFT size or leave it blank.
Select the window type you plan to use.
Enter a target frequency for bin comparison.
Click Calculate to show the result above the form.
Review true resolution, displayed spacing, ENBW, and nearest bin error.
Use CSV or PDF export for reporting or documentation.

FAQs

1. What is FFT frequency resolution?

FFT frequency resolution is the smallest frequency spacing that your captured time record can meaningfully distinguish. It mainly depends on observation time, not on the plotting style of the spectrum.

2. Does zero padding improve true resolution?

No. Zero padding only creates more frequency samples between existing spectral bins. It smooths the plotted spectrum and improves interpolation, but the true resolving power still comes from the original record length.

3. Why does a longer record improve resolution?

A longer record means more observation time. Since resolution equals 1 divided by record length, extending capture time reduces the minimum distinguishable frequency spacing.

4. What does Nyquist frequency mean here?

Nyquist frequency is half the sample rate. Frequencies above this limit fold back into the spectrum as aliases, so it defines the highest unambiguous frequency you can analyze.

5. Why do windows matter in FFT analysis?

Windows control spectral leakage. They trade amplitude accuracy, leakage suppression, and main lobe width. Some windows spread energy more, which can make closely spaced frequencies harder to separate.

6. What is ENBW?

Equivalent noise bandwidth estimates how much a chosen window broadens noise energy in frequency terms. It helps compare window effects beyond simple bin spacing.

7. Which value should I trust most?

For physical resolving ability, trust true frequency resolution based on record length. For plotted spectrum spacing and cursor steps, use displayed bin spacing based on FFT points.

8. Can two tones inside one bin still be separated?

Usually not reliably with a basic FFT alone. Separation depends on record length, signal-to-noise ratio, window choice, and whether advanced estimation methods are used.