Enhancing Infrared Thermometry Precision: A Wiener Transformation and Bayesian Parameter Estimation Approach

In myriad domains, infrared thermometry technology finds widespread application due to its inherent advantages, such as its contactless nature, extended range, and heightened sensitivity. However, the precision of infrared thermometry remains susceptible to environmental influences and emissivity variations, thereby hindering its further development. To elevate the accuracy of infrared thermometry, this research proposes a method grounded in the principles of Wiener transformation and Bayesian parameter estimation theory. Initially, a Wiener infrared temperature variation model incorporating random mixing effects is constructed based on the established principles of infrared thermometry. Subsequently, to address the challenge of parameter estimation within the model, a three-step iterative algorithm is devised employing the mean-field approximation theory to compute the model parameters. The model is then solved using these computed parameters to arrive at the target temperature. Finally, the feasibility of the proposed method is validated through rigorous numerical simulations and temperature measurement experiments conducted on aluminum alloy rings.