Fast Fourier Transform Algorithm. Chen Chen It is important to keep in mind at the outset that the FFT is NOT a new transform. It is simply a very efficient way to compute DFT.
Why the FFT ?
If you look at the equation for the Discrete Fourier Transform you will see that it is complicated to work out as it involves many additions and multiplications involving complex numbers. Even a simple eight sample signal would require 49 complex multiplications and 56 complex additions to work out the DFT. At this level it is still manageable, however a realistic signal could have 1024 samples which requires over 20,000,000 complex multiplications and additions. As you can see the number of calculations required soon mounts up to unmanageable proportions.
The Fast Fourier Transform is a simply a method of laying out the computation, which is much faster for large values of N, where N is the number of samples in the sequence. It is an ingenious way of achieving rather than the DFT's clumsy P2 timing.
The idea behind the FFT is the divide and conquer approach, to break up the original N point sample into two (N / 2) sequences. This is because a series of smaller problems is easier to solve than one large one. The DFT requires (N-1)2 complex multiplications and N(N-1) complex additions as opposed to the FFT's approach of breaking it down into a series of 2 point samples which only require 1 multiplication and 2 additions and the recombination of the points which is minimal.
The split-radix FFT is a fast Fourier transform (FFT) algorithm for computing the discrete Fourier transform (DFT), and was first described in an initially little-appreciated paper by R. Yavne (1968) and subsequently rediscovered simultaneously by various authors in 1984. (The name "split radix" was coined by two of these reinventors, P. Duhamel and H. Hollmann.) In particular, split radix is a variant of the Cooley-Tukey FFT algorithm that uses a blend of radices 2 and 4: it recursively expresses a DFT of length N in terms of one smaller DFT of length N/2 and two smaller DFTs of length N/4.
The split-radix FFT, along with its variations, long had the distinction of achieving the lowest published arithmetic operation count (total exact number of required real additions and multiplications) to compute a DFT of power-of-two sizes N.
References
R. Yavne, "An economical method for calculating the discrete Fourier transform," in Proc. AFIPS Fall Joint Computer Conf. 33, 115–125 (1968). P. Duhamel and H. Hollmann, "Split-radix FFT algorithm," Electron. Lett. 20 (1), 14–16 (1984). M. Vetterli and H. J. Nussbaumer, "Simple FFT and DCT algorithms with reduced number of operations," Signal Processing 6 (4), 267–278 (1984). J. B. Martens, "Recursive cyclotomic factorization—a new algorithm for calculating the discrete Fourier transform," IEEE Trans. Acoust., Speech, Signal Processing 32 (4), 750–761 (1984). P. Duhamel and M. Vetterli, "Fast Fourier transforms: a tutorial review and a state of the art," Signal Processing 19, 259–299 (1990). S. G. Johnson and M. Frigo, "A modified split-radix FFT with fewer arithmetic operations," IEEE Trans. Signal Processing 55 (1), 111–119 (2007). Douglas L. Jones, "Split-radix FFT algorithms," Connexions web site (Nov. 2, 2006). H. V. Sorensen, M. T. Heideman, and C. S. Burrus, "On computing the split-radix FFT", IEEE Trans. Acoust., Speech, Signal Processing 34 (1), 152-156 (1986).
