3 - Digital signal processing

Abstract

The digital signal processing (DSP) system design process comprises a mixture of empirical and ad hoc methods aimed at coping with large system complexity and minimizing the total cost. Typically, the resulting DSP system is composed of blocks representing well-known “standard” functions—for example, frequency selective filters, adaptive filters, correlation, spectral estimation, discrete Fourier and cosine transforms, and sample rate converters. The design of such basic DSP functions is therefore a topic of interest. It is useful to apply different design methodologies to some well-known DSP subsystems in order to better understand the advantages and disadvantages of a given approach. Such design studies may provide not only a better understanding of the strengths and weaknesses of the design methodology, but also a better insight into the computational properties of DSP algorithms. Further, they may provide points of reference and a basis for the design of more complex DSP systems. The objective of the first design iteration is to investigate the feasibility of the selected implementation approach and estimate major system parameters, such as power consumption and chip area. The major reason behind the increasing use of discrete-time and digital signal processing techniques is that problems caused by component tolerances as well as drift and aging of components are circumvented. For analog frequency selective filters, realizations having minimum circuit element sensitivity have been developed. Thus, by using high-quality components, high-performance filters can be implemented.