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Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Jae-Lim Lee¹, Dong-Sun Kim¹

¹Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

Accurate sensor calibration is crucial. This study introduces a segmented calibration method, significantly reducing errors and improving accuracy for reliable sensor operation, even with fewer data points.

Keywords:

2D calibration segmented calibration sensor calibration

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Area of Science:

Sensor Technology
Metrology
Calibration Techniques

Background:

Sensor nonlinearity directly impacts measurement accuracy, necessitating precise calibration for reliable operation.
Existing single-point calibration methods may struggle with complex nonlinearities across the entire input range.

Purpose of the Study:

To propose and evaluate a segmented calibration method for enhancing sensor accuracy.
To reduce computational complexity and improve calibration efficiency compared to traditional methods.

Main Methods:

The input range is divided into multiple segments, with optimized calibration functions determined for each section.
A modified progressive polynomial calibration technique is employed to calculate calibration functions, allowing for reduced polynomial degrees.
The method is extended to two dimensions to address cross-sensitivity errors.

Main Results:

The segmented calibration method achieved an error rate of 0.000006%, a substantial improvement over the single calibration method's 0.0823% error.
Accuracy is maintained with fewer calibration points, and computational complexity is reduced.
Two-dimensional simulations showed error rate reductions from 15.84% to 2.07% in an 8-bit system.

Conclusions:

Segmented calibration is an effective and scalable solution for improving sensor accuracy and reliability.
The method offers reduced computational load and enhanced performance, suitable for various sensor applications.
Extension to multi-dimensional calibration effectively mitigates cross-sensitivity errors.