Mathematical Method
Optimization algorithms that minimize the sum of squared differences between observed values and predicted values form the basis of sensor curve fitting. Implementing the least squares approach allows calibration software to define the most accurate linear or non-linear sensor response curve. This method isolates random measurement errors and provides a stable mathematical model of sensor behavior by ensuring that outliers do not disproportionately bias the calculated coefficients.
Regression Analysis
The calculation determines the coefficients of the calibration equation by solving a set of simultaneous linear equations. Calibration engineers input the raw output voltages and the corresponding reference values into the regression algorithm. The resulting line represents the best fit for the sensor across its entire operating range.
Calibration Error
Residual variations from the fitted curve indicate either sensor non-linearity or high levels of random noise. If the residuals show a systematic pattern, a higher-order polynomial might be required to model the sensor response. Plotting these residuals helps engineers identify systemic problems in the calibration setup.
Sensor Performance
High-precision transducers rely on these calculated coefficients to convert raw electrical signals into accurate physical measurements. The calibration coefficients are programmed directly into the sensor’s microchip. This mathematical correction ensures high accuracy during field deployment.
