群智能优化算法在基于自适应光学的FSO系统中的应用研究

发布时间：2018-01-05 04:33

本文关键词：群智能优化算法在基于自适应光学的FSO系统中的应用研究　出处：《吉林大学》2017年硕士论文　论文类型：学位论文

【摘要】：近些年来随着航天事业发展的需要,自由空间光通信(FSOC,Free space optical communication)获得飞速的发展。FSO通信系统通信潜力巨大,所以许多国家投入大量资金用于FSO通信技术的研究。FSO通信系统以其宽频带、低成本、无需频带认证等优点,迅速在深空、中轨道、地面站之间的通信中应用。通常情况下我们把卫星间的激光通信称为空间光通信,地面站之间的激光通信称为大气激光通信。FSO通信技术目前还面临一些问题:激光在大气中传输受到大气散射、大气吸收、大气湍流闪烁、大气湍流散斑以及大气多路效应的影响,造成光功率衰减、码间干扰、接收功率衰减、光斑中心漂移等现象,引起通信质量下降。目前,大气湍流影响是FSO通信实用化的最主要的问题之一。人们针对大气湍流影响进行大量理论探索和实验研究。近年来,利用自适应光学(AO,Adaptive optics)技术解决FSO通信系统中大气湍流的影响越来越受到人们的重视。前人针对AO技术在FSO中应用,提出很多经典算法,如随机并行梯度下降(SPGD,Stochastic parallel gradient descent)算法、模拟退火(SA,Simulated annealing)算法,爬坡法(HC,Hill climbing)以及遗传算法(GA,Genetic algorithm)等算法。这些算法中,SPGD算法的应用最为成熟。但是很少有人将群智能优化算法应用到FSO通信系统中。本文在分析大气湍流对FSO通信系统的影响的基础上,介绍实验用到的FSO通信实验系统,将粒子群算法(PSO,Particle swarm optimization)、蛙跳算法(SFLA,Shuffled frog leading algorithm)和混合蛙跳算法(MSFL,Modified shuffled frog leaping algorithm)应用到实验系统中。本文主要探究群智能优化算法对高阶像差的补偿。为了评价群智能优化算法的表现,我们把SPGD算法作为对比实验对象。在实验中经过多次探索,提出一种评价补偿效果的系统性能指标J。J与光斑的斯特列尔比(SR,Strehl rate)成正比,所以J能够正确评价补偿效果。另一方面,J计算方便,在实验中表现稳定,应用方便。实验分别探究了个体数目对PSO、SFLA和MSFL算法的影响。实验表明,个体数目影响PSO算法的全局收敛性,对SFLA和MSFL算法的全局收敛影响较小;个体数目的增加会加快三种算法的收敛速度。实验对比SPGD、MSFL、SFLA和PSO算法发现:在一定数目的前提下,群智能优化算法全局收敛的速度要远远快于SPGD算法;与SPGD算法在不同像差下收敛次数变化大并且可能出现局部收敛相比,群智能优化算法表现更加稳定,收敛速度变化不大并且不会局部收敛。对比三种群智能优化算法实验中的表现,当个体数目合适时,三种算法均能达到或者非常接近全局最优;对于相同初始像差个体数目相同时,MSFL算法的收敛速度最快。另一方面,在比较MSFL和SFLA算法发现,MSFL算法对“精英蛙”处理之后,可以解决SFLA算法中容易在收敛过程中在局部收敛过多的问题。
[Abstract]:In recent years, with the development of aerospace industry, the FSOC of free space optical communication has been developed. Free space optical communication has gained rapid development. The communication potential of FSO communication system is huge. Therefore, many countries invest a lot of money in the research of FSO communication technology. Because of its advantages of wide band, low cost, no band authentication and so on, FSO communication system is rapidly in deep space and middle orbit. The application of communication between earth stations. Usually we call the laser communication between satellites as space optical communication. The laser communication between earth stations is called atmospheric laser communication. FSO communication technology still faces some problems at present: laser propagation in the atmosphere is scattered by atmosphere, atmospheric absorption, atmospheric turbulence scintillation. The effects of atmospheric turbulence speckle and atmospheric multipath effect cause optical power attenuation, inter-symbol interference, reception power attenuation, spot center drift and so on, which lead to the decrease of communication quality. Atmospheric turbulence effect is one of the most important problems in the application of FSO communication. A large number of theoretical and experimental studies have been carried out on atmospheric turbulence effects. In recent years, adaptive optics have been used to study the effects of atmospheric turbulence. Adaptive techniques have been paid more and more attention to solve the influence of atmospheric turbulence in FSO communication system. According to the application of AO technology in FSO. Many classical algorithms are proposed, such as stochastic parallel gradient descent parallel gradient descenting algorithm. Simulated annealing (SA) algorithm, climbing method (HCH Hill climbing) and genetic algorithm (GA). Genetic algorithms and so on. The application of SPGD algorithm is the most mature, but few people apply swarm intelligence optimization algorithm to FSO communication system. This paper analyzes the influence of atmospheric turbulence on FSO communication system. The FSO communication experiment system used in the experiment is introduced. The particle swarm optimization algorithm (PSO) is introduced. The particle swarm optimization (PSO) algorithm and the breaststroke algorithm (SFLA) are introduced. Shuffled frog leading algorithm and hybrid leapfrog algorithm MSFL. Modified shuffled frog leaping algorithm). In order to evaluate the performance of swarm intelligence optimization algorithm, this paper mainly discusses the compensation of high order aberration by swarm intelligence optimization algorithm. We take the SPGD algorithm as the contrast experiment object. After many explorations in the experiment, we propose a system performance index J.J and the Steller-Beamer SR of the spot to evaluate the compensation effect. Strehl rate is proportional, so J can evaluate the compensation effect correctly. On the other hand, J is easy to calculate and stable in the experiment. The effect of the number of individuals on the PSO-SFLA and MSFL algorithm is investigated in the experiment. The experiment shows that the number of individuals affects the global convergence of the PSO algorithm. It has little influence on the global convergence of SFLA and MSFL algorithms. The increase of the number of individuals will accelerate the convergence rate of the three algorithms. The global convergence speed of swarm intelligence optimization algorithm is much faster than that of SPGD algorithm. Compared with the convergence times of SPGD algorithm under different aberrations and the possibility of local convergence, the swarm intelligence optimization algorithm is more stable. When the number of individuals is suitable, the three algorithms can reach or very close to the global optimum. For the same initial aberration with the same number of individuals, the convergence rate of the MSFL algorithm is the fastest. On the other hand, after comparing the MSFL and SFLA algorithms, we find that the MSFL algorithm deals with the "elite frog". It can solve the problem of local convergence in SFLA algorithm.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.1;TP18

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