Optimal synthesis of smart measurement systems with adaptive correction of drifts and setting errors of the sensor's working point

This paper deals with a problem in optimal synthesis of smart measurement systems. A nonlinear model of the sensor is introduced, and directly applied to the derivation of optimal algorithms for processing measurement data. The analysis is performed for systems with both noncompensatory and compensatory sensors. It is assumed that a measurement is performed in the presence of random disturbances and noise. The algorithms developed take into account the limited input-output range of the sensor as well as the influence of its internal noise on measurement quality. Simultaneously, they automatically and optimally correct errors in the initial setting of the sensor's working point, as well as those caused by its random drift.