用于分布式光纤传感系统的混合算法研究

在基于马赫-曾德干涉(MZI)原理的分布式光纤 扰动传感系统中,为了减小由于偏振态退化引起的定位误差, 提出了一种基于模拟退火混沌粒子群(SCPSO)算法的偏振控制算法,阐述了算法的优化过程 。理论分析和实验结果 表明,相对于优化前的智能优化方法,优化后的方法提高了收敛效率和收敛精度, 并有效提高了系统定位精度,使得系统定位误差控制在±10m以内 ,完成优化所需迭代次数小于9次。

Research on mix algorithm applied in distributed optical fiber sensor

To suppress the positioning errors induced by the polarization fading in distrib uted optical fiber perturbation sensor based on dual Mach-Zehnder interferomete rs,the optimization algorithm used in the polarization control of the distribute d optical fiber perturbation sensor is studied,the inapplicable part in the part icle swarm algorithm is improved,which affects the polarization adjustment of th e distributed optical fiber perturbation sensor,and a polarization control algor ithm based on simulated annealing chaotic particle swarm optimization (SCPSO) is proposed.The design concept and implementation process of the algorithm are int roduced.Then the algorithm is analyzed theoretically and evaluated experimentall y.The experiments are conducted in the mountainous areas where the natural envir onment is relatively bad.To adjust the polarization state of the distributed opt ical fiber perturbation sensor using this mix algorithm in the way of feedback c ontrol,the difference b etween the two interference signals is made to be the adaptive value of the pola rization control algorithm.Theory analysis and experiment results show that the mix algorithm improves the convergence accuracy and convergence efficiency relat ive to basic intelligent optimization methods,and improves the positioning accur acy effectively.The iteration times are less than 9to coplete the optimizaiton, and the positioning error of the distributed optical fiber perturbation sensor c an be restricted within ±10m.