Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization

Abstract

This study presents a fast and accurate data processing method for multispectral radiation thermometry that can accurately measure the true temperature of steel materials without requiring a priori emissivity model. The method generates a temperature matrix by inputting emissivity values at different wavelengths and selects a reference vector from the matrix. Then, it rearranges the temperature matrices at other wavelengths and calculates the Euclidean distance between each column element of the rearranged matrix and the reference vector. The method uses an unconstrained optimization technique to minimize the Euclidean distance and obtain the true temperature and emissivity of the object simultaneously. We evaluate the performance of the method by simulation and experiment in the response band of 1.4 ∼ 2.5 µm and temperature range of 873 ∼ 1173 K. The simulation results indicate that the relative error of the inverted temperature is within 0.229%, and the average computation time is less than 112.301 ms. The experimental results show that the maximum temperature error during the measurement process is 0.813%. Our method provides a feasible and efficient solution for real-time temperature measurement of steel materials.