Sensors and Measurements Design Optimization: Revisiting Resolution From Information Theory Viewpoint

The concept of resolution is fundamental to any sensing or measurement system, as it establishes a reference for the intrinsic detection limit of any acquisition process. Although this concept originally emerged in the context of DC measurements, it should be more clearly defined in a generic acquisition system, where the signal is inherently stochastic. This article revisits the sensing and measurement process through the lens of information theory, where resolution is considered in relation to the distributions of random variables. In addition, it will introduce a new parameter, the “number of resolution levels (NL),” which is valuable because it encapsulates the amount of information conveyed by a measurement system. The results demonstrate that how this framework can address key design questions, such as determining the optimal discretization for an analog-to-digital (A/D) converter given input-referred noise from a transducer, and how to optimize the system from a resolution perspective. Theoretical results will be compared with numerical simulations of an analog noisy interface coupled with an A/D converter.