Howtosignalprocessing
- Why do we use overlap in FFT?
- How many samples do I need for FFT?
- How many points do you need for FFT?
- What is the domain of the FFT?
Why do we use overlap in FFT?
FFT processing can be particularly problematic when the signal consists of randomly occurring transients superimposed on a more continuous signal. Overlap processing is commonly used in this situation to improve the estimates.
How many samples do I need for FFT?
The number of samples (N) in the FFT must be an integer power of 2. Therefore, N = 2p, where p is a positive integer. This rule minimizes the number of multiplications—and therefore the computation time—needed to compute the coefficients of the Fourier series.
How many points do you need for FFT?
Because the FFT function uses a base 2 logarithm by definition, it requires that the range or length of the time series to be evaluated contains a total number of data points precisely equal to a 2-to-the-nth-power number (e.g., 512, 1024, 2048, etc.).
What is the domain of the FFT?
An FFT transform deconstructs a time domain representation of a signal into the frequency domain representation to analyze the different frequencies in a signal. The frequency domain is great at showing you if a clean signal in the time domain actually contains cross talk, noise, or jitter.
