2 - Intuitive digital signal processing

Abstract

This chapter introduces the basic concepts of digital signal processing (DSP) and filtering, a technique that is commonly used to extract the desired information from noisy data. A thorough knowledge of DSP is invaluable to the development of robust sensor systems. The chapter analyzes signals in both the time and frequency domains. Depending upon the characteristics of the signal of interest and the noise, it is possible to accurately extract the signal of interest even in the presence of significant levels of noise using certain techniques. Any real analog signal can be represented in the frequency domain via a mathematical operation known as the Fourier transform, and the proper choice of domain (either the time domain or the frequency domain) can greatly simplify the analysis of a particular signal-processing situation. When analyzing a specific sensor signal-processing application, certain aspects of the system are considered: the physical property to be measured, the relationship between the physical property being measured and the corresponding parameter value to be reported, the expected frequency spectrum of the signal of interest and any noise sources in the environment, the physical characteristics of the operating environment, any error conditions that may arise and the proper technique for handling them, the calibration requirements, user and/or system interface requirements, and the maintenance requirements.