Likelihood-based Sensor Calibration using Affine Transformation
This work addresses sensor calibration for applications like software calibration and expert-based adaptation, but it is incremental as it builds on a 1973 solution.
The paper tackles the problem of calibrating sensors by adapting measurements between identical sensors using an affine transformation, and it demonstrates improved results in both simulations and real data experiments.
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.