Research on Multi-spectral Temperature Measurement Method based on Emissivity Model Recognition Optimization

The multi-spectral radiation temperature measurement technology faces problems of uncertain target emissivity models and large temperature measurement errors. This paper proposes a multi-spectral radiation temperature measurement method based on optimized identification of emissivity models. Firstly, an emissivity model is established. Secondly, the particle swarm optimization algorithm is used to train the least squares support vector machine. Then, this algorithm is used to identify the target emissivity model, which has stronger applicability and accuracy compared to existing identification methods of emissivity models. Finally, a multi-spectral target equation is constructed and the particle swarm algorithm is used to invert the target temperature. Simulation experimental results show that this method can obtain more accurate emissivity models and temperature inversion, improving the generality and stability of the multi-spectral radiation temperature measurement method. Simulation and existing experimental comparisons show that the proposed multi-spectral radiation temperature measurement method based on optimized identification of emissivity models performs well in the identification of target emissivity models and temperature measurement, providing an effective solution for practical temperature measurement.