Likelihood-based Sensor Calibration using Affine Transformation

TL;DR

This paper improves an affine transformation-based sensor calibration method, demonstrating its effectiveness for software calibration, expert-based adaptation, and future distributed learning applications through simulations and real data experiments.

Contribution

The paper presents an enhanced affine transformation approach for sensor calibration, integrating expert knowledge and validating it with simulations and real multi-sensor data.

Findings

Improved calibration accuracy in simulations

Successful application to real multi-sensor data

Demonstrated adaptability for various sensor calibration scenarios

Abstract

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an affine transformation between different systems. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. Both data set and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.