In any data acquisition system (DAS) many error effects, both of systematic nature (e.g. nonlinearity) and of random nature (e.g. electronic noise) are simultaneously present. While systematic errors are a comparatively stable characteristic of a DAS, random errors may be smaller or larger in different situations, and it is important to understand how they degrade the overall performance of the system. It is even more important to understand that random errors can be actually used to improve the fidelity of the acquisition, i.e. the technique of dithering. This possibility is due to the inherent presence in any DAS of a particular kind of error: the quantization error. Quantization is a basically simple operation and it is easily understood at an elementary level. However, evaluating its effects on signals, with or without the simultaneous presence of other errors, requires quite complex mathematics, usually not mastered by engineers and even by researchers without a specific interest in the topic. Due to the complexity of the subject (an excellent reference book is [WK08]), misunderstandings and mistakes are common when dealing with noise in DAS. For example, it is true that averaging a particular number of samples is convenient to reduce the noise, but it is easy to disregard the fact that it is useless to increase the number of samples beyond a certain limit (contrary to what happens in analogue measurements). In the same way, even if introducing noise in a DAS may be desirable and effective, and is expressly a feature in commercial DAS (e.g. [Nat97], [Nat07]), few users are aware of how the appropriate level of noise (and other parameters) can be chosen. The present chapter deals with the topic of performance degradation/improvement in a DAS, deriving by the presence (wanted or unwanted) of noise, and by averaging or filtering the output samples. The aim is making the theory understandable and usable by a wide audience, using ideas and mathematics as simple as possible. Proper reference, when needed, is made to works with rigorous mathematical demonstration of the derived results. The chapter covers only the case of perfectly linear DAS, with no (or negligible) nonlinearity errors. The more general case of nonlinear DAS with noise is a subject for a possible future expanded version of the chapter.
|Autori interni:||ATTIVISSIMO, Filippo|
|Titolo:||Noise, Averaging, and Dithering in Data Acquisition Systems|
|Titolo del libro:||Data Acquisition|
|Nome editore:||Vadursi Michele|
|Data di pubblicazione:||2010|
|Digital Object Identifier (DOI):||10.5772/10461|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|