基于改进粒子群算法拟合大气光学湍流廓线模式的研究  

Atmospheric Optical Turbulence Profile Model Fitting Based on Improved Particle Swarm Algorithm

作　　者：冯克涛 李晓毅 钱璇[2] 吴乐华 郑鹤 陈谋 李梦如 刘博[3] Feng Ketao;Li Xiaoyi;Qian Xuan;Wu Lehua;Zheng He;Chen Mou;Li Mengru;Liu Bo(Communication Sergeant School,Army Engineering University of PLA,Chongqing 400035,China;National Astronomical Observatory,Chinese Academy of Sciences,Beijing 100101,China;Unit 78092 of PLA,Chengdu,Sichuan 610036,China)

机构地区：[1]陆军工程大学通信士官学校,重庆400035 [2]中国科学院国家天文台,北京100101 [3]中国人民解放军78092部队,四川成都610036

出　　处：《激光与光电子学进展》2022年第5期73-84,共12页Laser & Optoelectronics Progress

基　　金：军内科研项目(KYCQJWJK1703)。

摘　　要：提出一种改进自适应粒子群算法并将其应用于大气光学湍流廓线模式的拟合研究。为提高粒子群算法的寻优速度,避免陷入局部最优,提出一种改进的自适应粒子群算法。采用当前粒子与全局最优位置的距离来调控惯性权重系数,进行非线性自适应变化;采取对称线性变化思想设计自我学习因子和社会学习因子,实现了各阶段寻优重点的自适应改变。把改进的自适应粒子群算法引入到求解阿里地区广义Hufnagel-Valley湍流模式中,拟合得到该地区早晚和四季的湍流模式廓线。仿真表明,本文算法的判定系数均在0.997以上,这与探空获得的统计平均廓线保持一致。对比其他自适应粒子群算法,本文算法的收敛精度基本一致但速度更快。本研究为基于Hufnagel-Valley湍流廓线模式拟合提供了新方法。This work improves and applies the adaptive particle swarm optimization algorithm to the study of statistical model fitting of atmospheric turbulence profiles. First, an improved adaptive particle swarm optimization algorithm is proposed to improve the speed of particle swarm optimization and avoid falling into the local optimum.The distance between the current particle and the global optimal position is used to adjust the inertia weight coefficient and make nonlinear adaptive changes. The self-learning and social learning factors are based on the concept of symmetrical linear change to realize the adaptive change of the optimization focus in each stage. Second,the improved adaptive particle swarm optimization algorithm is introduced to solve the generalized Hufnagel-Valley turbulence model in Ali region, and the turbulence model profiles of morning, evening, and four seasons in the region are fitted. The simulation results show that all the decision coefficients are greater than 0. 997, which agrees well with those of the statistical average profiles obtained by radiosonde. The proposed method has similar convergence accuracy to other adaptive particle swarm optimization algorithms, but the speed is faster. This paper introduces a new method for fitting Hufnagel-Valley turbulence profile models.

关 键 词：大气光学 大气折射率结构常数 湍流廓线模式 改进的粒子群优化算法 

分 类 号：O439[机械工程—光学工程] P427.1[理学—光学]