The modelling of nonlinear distance sensor using piecewise newton polynomial with vertex algorithm

Abstract

The Sharp GP2Y0A02YK0F is categorized as a nonlinear sensor for distance measurement. This sensor is also categorized as a low-cost sensor. The higher resolution, cheap, high accuracy and easy to install are the advantages. The accuracy level of this sensor depends on the type of the measured object materials, requires an additional device unit and further processing is required since the output is non-linear. The distance determination is not easy for this type of sensor since the characteristic of this sensor fulfills non-injective function. The modelling process is one of methods to convert the output voltage of the sensor to a distance unit. The advantages of polynomial modelling are simple form model, moderate in flexibilities of shape, well known and understood properties, and easy to use for computational matters. The obstacle of polynomial-based modelling is the presence of Runge’s phenomenon. The minimization of Runge’s phenomenon can be done with decreasing the model order. The piecewise Newton polynomials with vertex determination method have been succeeded to generate a nonlinear model and minimize the occurrence of Runge’s phenomenon. The low level of MSE by 0.001 and error percentage of 2.38% has been obtained for the generated model. The low MSE level leads to the high accuracy level of the generated model.

Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods

“…The voltage at different distances was measured versus time, where the voltage-distance relationship showed nonlinearity, while the voltage-inverse distance showed linearity in some regions. In another work, the GP2Y0A02YK0F was modelled by using the piecewise Newton polynomials with the vertex determination method to generate a nonlinear model, which was successful in minimizing the Ruge's effect that appears in polynomial-based modelling [18]. The mean square error was as low as 0.001 and the percentage error was 2.38%.…”