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<p>The paper is devoted to non-contact measurements for temperature and emissivity. There are many parameters controlling emissivity: temperature, wavelength, surface condition, etc. There are cases for insufficiently detailed approximation for a body with emissivity depends on the wavelength as an issue of processing the remote measurements of emissivity. A hybrid method aimed to reducing the multidimensional minimization to one-dimensional case for calculating the emissivity decomposition coefficients is presented. For a gray body, linear and quadratic emissivity models are considered. An increasing number of solutions with increasing number coefficients of polynomial decomposition for emissivity is observed. The approach of choosing a proper solution is developed. The limitations on the model parameters numbers are presented: this is related to a growth of relative uncertainties in each parameter and with using the Akaike criterion.</p>

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Determination of emissivity dependence on wavelength using a hybrid least squares method for non-contact temperature measurements

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • P. V. Zinin,
  • P. A. Grishaev,
  • K. M. Bulatov

摘要

The paper is devoted to non-contact measurements for temperature and emissivity. There are many parameters controlling emissivity: temperature, wavelength, surface condition, etc. There are cases for insufficiently detailed approximation for a body with emissivity depends on the wavelength as an issue of processing the remote measurements of emissivity. A hybrid method aimed to reducing the multidimensional minimization to one-dimensional case for calculating the emissivity decomposition coefficients is presented. For a gray body, linear and quadratic emissivity models are considered. An increasing number of solutions with increasing number coefficients of polynomial decomposition for emissivity is observed. The approach of choosing a proper solution is developed. The limitations on the model parameters numbers are presented: this is related to a growth of relative uncertainties in each parameter and with using the Akaike criterion.