Fast Fourier Transform
- The fast Fourier transform using radix-2 and radix-4
- Decimation or decomposition in frequency and in time
- Programming examples
The fast Fourier transform (FFT) is an efficient algorithm that is used for converting a time-domain signal into an equivalent frequency-domain signal, based on the discrete Fourier transform (DFT). Several real-time programming examples on FFT are included.
The DFT converts a time-domain sequence into an equivalent frequency-domain sequence. The inverse DFT performs the reverse operation and converts a frequency-domain sequence into an equivalent time-domain sequence. The FFT is a very efficient algorithm technique based on the DFT but with fewer computations required. The FFT is one of the most commonly used operations in digital signal processing to provide a frequency spectrum analysis [1–6]. Two different procedures are introduced to compute an FFT: the decimation-in-frequency and the decimation-in-time. Several variants of the FFT have been used, such as the Winograd transform [7, 8], the discrete cosine transform (DCT) , and the discrete Hartley transform [10–12]. The fast Hartley transform (FHT) is described in Appendix E. Transform methods such as the DCT have become increasingly popular in recent years, especially for real-time systems. They provide a large compression ratio.
6.2 DEVELOPMENT OF THE FFT ALGORITHM WITH RADIX-2
The FFT reduces considerably the computational requirements of the DFT. The ...