ABSTRACT
Filters are closely related to spectral analysis since the goal of filtering is to reshape the spectrum to one’s advantage. Most noise is broadband (the broadest-band noise being white noise with a flat spectrum) and most signals are narrowband; hence, filters that appropriately reshape a waveform’s spectrum will almost always provide some improvement in SNR. As a general concept, a basic filter can be viewed as a linear process in which the input signal’s spectrum is reshaped in some well-defined (and, one hopes, beneficial) manner. Filters differ in the way they achieve this spectral reshaping, and can be classified into two groups based on their approach. These two groups are termed finite impulse response (FIR) filters and infinite impulse response (IIR) filters, although this terminology is based on characteristics which are secondary to the actual methodology. We will describe these two approaches separately, clarifying the major differences between them. As in preceding chapters, these descriptions will be followed by a presentation of the MATLAB implementation.
