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一设计传感器间的测量适配流程。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。本文提出了冰川研究中心1973年发表的一种改进方案。结果表明，该方案在传感器软件校准、基于专家知识的适配实现等应用中具有广泛适应性，并为分布式学习方法等未来技术发展奠定了基础。其核心理念在于利用专家知识估计不同系统间的仿射变换。我们通过仿真实验以及搭载8个相同传感器的多传感器板实测数据对研究进行验证。相关数据集与评估脚本均已公开提供。实验结果表明，该方法在仿真与真实数据实验中均实现了性能提升。