Improved progressive polynomial algorithm for self-calibration and optimal response in smart sensors

The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems expend the least possible amount of time in their calibration. An autocalibration algorithm for smart sensors should be able to fix major problems such as offset, variation of gain and no linearity, as accurately as possible. This paper shows the performance of a progressive polynomial algorithm under different grades of relative nonlinearity of an output signal sensor. Besides, it presents an improvement to this algorithm to obtain the optimal response and minimum nonlinearity error, based on the number and selection sequence of the calibration points. In order to verify the potential of the proposed criteria, a temperature measurement system was designed. The system is based on a thermistor, which presents one of the worst nonlinearity behavior, and it is also found in many real world applications because of its economical price. The application of the proposed method to this system showed that an adequate sequence of the calibration points yields to the minimum nonlinearity error.In real world applications, by knowing the grade of relative nonlinearity of a sensor, the number of calibration points can be determined using the proposed method in order to obtain the desired nonlinearity error. This will impact on autocalibration methodologies and their associated factors: like time and cost.