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Likelihood-Based Sensor Calibration Using Affine Transformation
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DOI：10.1109/JSEN.2023.3341503.png)
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[] 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 (AT) between different systems, which can be improved by the knowledge of experts. This article 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 AT between different systems. We evaluate our research with simulations and also with real measured data of a multisensor board with eight identical sensors. Both dataset and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.
Keyword:
Sensors
Maximum likelihood estimation
Estimation
Sensor systems
Data models
Adaptation models
Calibration
Distributed learning
expert supported learning
sensor adaptation
transformation of sensors
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
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