What Techniques Are Used for Digital Filtering?
Using the FFT (fast-Fourier transform) algorithm is one of the most popular ways to convert a series of digital samples from the time domain to the frequency domain. The result of an FFT looks like the output of a spectrum analyzer. Moreover, because the FFT works in both directions, you should be able to digitally filter a signal just by taking its FFT, modifying its spectrum directly, and converting it back to time-domain data. The FFT assumes that the spectrum does not change over time. Furthermore, if the input signal is not synchronized to the ADC’s sampling clock, the spectrum will become smeared, thus obscuring detail. There are other DSP (digital-signal-processing) techniques that are more advantageous for filtering a real-world signal. The IIR (infinite-impulse-response) and FIR (finite-impulse-response) filters can be implemented very inexpensively, and they work on a continuous stream of data. The wavelet transform is worth investigating. Like the FFT, it converts time-domai
