A conventional method for frequency detection is Fourier analysis.
Matlab implementation of two common fractional step projection methods is considered: the 2d Fourier pseudo-spectral method, and the 3d finite difference method with 2nd order spatial accuracy.
However, one drawback of Fourier methods is the difficulty in computing transient responses with arbitrary initial conditions (ICs).
One of the earliest, yet most efficient global sensitivity analysis methods is the variance-based Fourier amplitude sensitivity test (FAST) [ 26].
Since the main step of FTVd method is fast Fourier transforms (FFTs) for each iteration, we can control the iterative number to get the faster speed and more accurate results than the decovolution method in [22].
The third method is fast Fourier transform (FFT -based acquisition/correlation, which is based on the FFT -basedcorrelacquisition/correlationication in the frequency (FFT) domain [1, 3].
The advantage of the double Fourier method is that fast Fourier transforms, which have a computational complexity of O N2logN), can be used in both the longitudinal and latitudinal directions.
Standard methods are derived from Fourier analysis.
Intuitively, Fourier methods are very close to X-ray or electron crystallography, where data are collected to fill Fourier space so that the inverse Fourier transform generates the 3D map of the object in real space.
The method is based on Fourier techniques, and in particular it makes use of a modified unequally spaced fast Fourier transform algorithm, in combination with previously suggested divide and conquer strategies for ordinary fast Gauss transform methods.
