针对红外辐射计标定中的数据拟合问题,提出了一种基于PSO-AMLS算法的数据拟合方法.该方法结合了粒子群算法(PSO)和自适应移动最小二乘法(AMLS).采用的PSO算法可以实现自适应移动最小二乘法中参数的最优化,提高了红外辐射计的校准精度.区别于传统的应用于红外辐射计校准中的数据拟合方法,文中提出的基于局部近似的PSO-AMLS数据拟合方法,不仅可以得到红外辐射计输出电压与准直辐射源辐射之间的对应关系,而且在数据处理中通过灵活的数据处理机制,可以降低因拟合数据不均匀分布对拟合结果产生的不利影响.实验结果表明,文中提出的PSO-AMLS数据拟合方法,优于红外辐射计校准中所用的传统数据拟合方法,可以提高数据拟合精度.“,”A hybrid PSO-AMLS-based method for data fitting in the calibration of the infrared radiometer was described.The proposed method was based on Particle Swarm Optimization (PSO) in combination with Adaptive Moving Least Squares (AMLS).The optimization technique involved parameters setting in the AMLS fitting,which significantly influenced the fitting accuracy.However,its use in the calibration of the infrared radiometer has not been yet widely explored.Bearing this in mind,the PSO-AMLS-based method,which was based on the local approximation scheme,was successfully used here to get the relationship between the radiation of the standard source and the output voltage of the infrared radiometer.The main advantages of this method were the flexible adjustment mechanism in data processing and the ability in reducing the adverse effect resulting from the non-uniform distribution of fitting data.Numerical examples and experiments are performed to verify the superior performance of the PSO-AMLS-based method compared to the conventional data fitting method.