基于广义逆-坐标变换的多光谱辐射测温反演算法

Multi-Spectral True Temperature Inversion Algorithm Based on Generalized Inverse Matrix-Coordinate Rotation Method

多光谱辐射测温在高温测量领域应用广泛。但是,未知的光谱发射率是多光谱辐射测温反演过程的最大困难。目前,解决方法多采用假设发射率模型法,二次测量法等,此类方法反演精度取决于假设的发射率模型和实际发射率是否相符,多数情况下反演结果误差较大。基于约束优化的多光谱辐射温度数据处理算法解决了未知发射率的难题,但受迭代算法的复杂性和初值难以确定的影响,反演精度和效率不高。为此,提出广义逆-坐标轮换算法解决约束优化算法中的反演效率问题。由于广义逆法需对发射率范围进行约束,坐标轮换法需设定合适的发射率初值,考虑两种算法各自的优势与不足,可对两种算法进行结合。将广义逆法求得的最小范数解作为约束优化算法中迭代搜索的初始点,进一步提高了算法对不同材料发射率的适应度。为验证算法是否能在无需考虑发射率模型的前提下寻找符合待测目标的发射率和真温,选取六种不同发射率类型的目标材料进行仿真实验。针对六种典型材料的仿真结果表明,新方法在真温1 800 K的情况下,绝对误差和相对误差均小于5.0%,与梯度投影法相比运算效率平均提高了202倍。表明该算法具有无需考虑发射率模型、反演精度高,速度快,适合于各类材料等优点,解决了约束优化算法中初值选择不确定的问题,为在线实时高温测量中的数据处理提供了解决方案。

Multi-spectral radiation temperature measurement measures multiple spectral radiation intensity information of a certain point of the object to be measured and obtain the true temperature through the Planck formula inversion.However,the unknown spectral emissivity is the biggest obstacle to the inversion process of multi-spectral radiation temperature measurement.At present,a set of emissivity models(emissivity-wavelength or emissivity-temperature models)are often used in advance.If the assumption model matches the actual situation,the inversion result can meet the requirements.If the assumption model does not match the actual situation,the reverse result of the performance is a very error.Whether it can realize the direct inversion of true temperature and spectral emissivity without any hypothetical model of spectral emissivity has always been a hot and difficult point in the theoretical research of multi-spectral radiation temperature measurement.For this reason,a generalized inverse matrix-coordinate rotation algorithm is proposed to transform the inversion problem of multi-spectral radiation temperature measurement into a constrained optimization problem.Since the generalized inverse method needs to constrain the emissivity range,and the coordinate rotation method needs to set a proper initial emissivity value,considering the respective advantages and disadvantages of the two algorithms,the two algorithms can be combined.The minimum norm solution obtained by the generalized inverse method is used as the initial point of the iterative search in the constrained optimization algorithm,which further improves the adaptability of the algorithm to the emissivity of different materials.In order to verify whether the algorithm can find a special solution that meets the thermophysical parameters(emissivity)and true temperature of the target under test without considering the assumed relationship between emissivity and wavelength,six types of targets with a representative emissivity change trend are selected Material is simulated experiment.The simulation results of six different spectral emissivity models show that the new algorithm does not require any prior knowledge about emissivity,and the inversion results of different emissivity models perform well.In the case of a true temperature of 1800 K,the absolute error and relative errors are less than 5.0%.Compared with the gradient projection method,the calculation efficiency is increased by 202 times on average.It shows that the algorithm has the advantages of not considering any prior knowledge of spectral emission rate,fast inversion speed and being suitable for various emission models.It further improves the theory of multi-spectral radiation temperature measurement and has good application prospects in high-temperature measurement.