Likelihood-based Sensor Calibration using Affine Transformation

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 kn...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Machhamer, Rüdiger, Lejla Begic Fazlic, Guven, Eray, Junk, David, Gunes Karabulut Kurt, Naumann, Stefan, Didas, Stephan, Klaus-Uwe Gollmer, Bergmann, Ralph, Timm, Ingo J, Dartmann, Guido
Format: Article
Language:English
Subjects:
Adaptation
Affine transformations
Algorithms
Calibration
Glaciers
Machine learning
Sensors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2309.11526