Constraint optimization algorithm for spectral emissivity calculation in multispectral thermometry

Abstract In multispectral thermometry, spectral emissivity calculation is crucial for temperature measurement. Constraint optimization algorithms require a shrunken emissivity search range and an appropriate initial solution to ensure high accuracy of temperature calculation. We propose a novel method without these requirements. In our method, the trend of the emissivity−wavelength curve is utilized to establish constraints, and a genetic algorithm is utilized as the optimization tool. Experiments reveal that the proposed method significantly improves the accuracy of temperature calculation for multispectral thermometry within a relative error of 1%. Our method can be applied to some high-precision temperature measurements (that require relative error less than 1%), such as the measurement of subtle variations in temperature distribution for aerospace engine plumes, and the measurement of slight changes in temperature during laser processing.