Multispectral thermometry data processing algorithm combining augmented Lagrangian method and modified equilibrium optimizer algorithm

This paper proposes an algorithm for the data processing of multispectral thermometry suitable for high temperatures ranging from 1500 to 3000 K. The algorithm is based on Planck's law, transforming the temperature inversion problem into an equality-constrained optimization problem and employing the augmented Lagrangian method (ALM) as the solution approach. To address the issue of ALM being greatly influenced by the initial iteration point, this paper proposes the modified equilibrium optimizer algorithm to provide the emissivity initial point for ALM, forming the modified equilibrium optimizer-augmented Lagrangian method. Simulation experiments for hypothetical targets, tungsten, and solid rocket engine plumes have demonstrated the effectiveness and superiority of the proposed algorithm, offering a feasible and efficient solution for the field of multispectral high-temperature online real-time temperature measurement. In addition, the experiment on 2219 aluminum alloy has demonstrated the effectiveness of the algorithm in practical applications.