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.

翻译：传感器技术领域的一项重要任务是高效实现同型号传感器间测量值的适配流程。一种思路是利用不同系统间仿射变换的估计方法，该过程可通过专家知识加以改进。本文提出了一项源于冰川研究所在1973年发表的改进方案。研究结果表明，该方案可适配多种应用场景，包括传感器软件校准、基于专家知识的适配实现，并为分布式学习等未来技术发展奠定基础。其中一个关键思路是利用专家知识估计不同系统间的仿射变换。我们通过仿真实验及搭载8个同型号传感器的多传感器电路板实测数据进行验证。数据集与评估脚本均提供公开下载。结果显示，无论是仿真环境还是真实数据实验，该方法均表现出性能提升。