Tian Zhuangtao, Zhang Kaihua, Xu Yanfen, Yu Kun, Liu Yufang
Opt Express. 2022 Sep 26;30(20):35381-35397. doi: 10.1364/OE.470056.
The data processing in multispectral thermometry remains a huge challenge due to the unknown emissivity. In this article, a novel data processing model of multispectral thermometer is established by adding new constraints of emissivity on the basis of object function. The new two algorithms for model optimizing, Sequential Randomized Coordinate Shrinking (SRCS) and Multiple-Population Genetic (MPG), are introduced. The temperature and emissivity of two samples are calculated by MPG algorithm to prove the validity of the MPG algorithm in practical application. The experiments reveal that the relative error of temperature is within 0.4% with the average calculation time of 0.36 s. The method proposed in this article can realize the simultaneous estimation of temperature and emissivity without emissivity assumption model, which is expected to be applied to real-time measurement of temperature in industrial fields.
由于发射率未知，多光谱测温中的数据处理仍然是一个巨大的挑战。在本文中，通过在目标函数的基础上添加发射率的新约束，建立了一种新颖的多光谱温度计数据处理模型。介绍了两种用于模型优化的新算法，即顺序随机坐标收缩（SRCS）和多群体遗传（MPG）算法。通过MPG算法计算了两个样品的温度和发射率，以证明MPG算法在实际应用中的有效性。实验表明，温度相对误差在0.4%以内，平均计算时间为0.36秒。本文提出的方法无需发射率假设模型即可实现温度和发射率的同时估计，有望应用于工业领域的温度实时测量。
