Reconstruction of Pressure Sensor Input Signal Based on Bivariate Polynomial Fitting Method

To overcome the output nonlinearity and temperature drift of pressure sensor,the method to reconstruct the input pressure with sensor output and sensor temperature using bivariate polynomial is proposed.After given the reconstruction model,the coefficients matrix in the model is computed from sensor static character calibration data by the least square method,and the best model structure is chosen according to three performance indexes: maximum fitting error,sum of squared errors and model order.The method to estimate resolution of measuring circuits is presented based on the model obtained.The advantage of the method proposed here is that it provides a way to get a consistent expression at the range of sensor calibration data,which eliminates the effect of output nonlinearity and temperature drift of the sensor and demands least data stored than the interpolation.Application on a type of silicon resonant pressure sensor validates the rationality and affectivity of this method.