基于优化粒子群算法的光纤传感谱形复用技术

Optical fiber sensing spectral shape multiplexing technology based on particle swarm optimization algorithm

大型光纤布喇格光栅(FBG)传感网络中,由于光源带宽有限,而FBG传感器复用数量多,会出现光谱重叠问题。因此,提高复用能力成为传感网络设计中的一个关键问题。结合谱形复用与量子粒子群优化(QPSO)算法,对多FBG传感系统进行实验与仿真分析。与传统粒子群算法相比,QPSO算法中参数设置比较简单,粒子搜索最优解能力大大提高。对QPSO算法和自适应变异的量子粒子群优化(AMQPSO)算法的解调结果进行分析,对比两者的解调精度与时间。AMQPSO算法在多FBG复用系统中的解调误差不超过3 pm,温度测量精度达0.3℃,解调时间不超过5 s,解决了多FBG系统中部分重叠及完全重叠问题。

In large fiber Bragg grating(FBG)sensor networks,due to the limited bandwidth of light sources and the large number of FBG sensors,the problem of spectral overlap will occur.Therefore,improving the reuse ability has become a key problem in the design of sensor networks.In this paper,the experiment and simulation analysis of multi-FBG transmission system are carried out by combining spectral reuse with quantum particle swarm optimization(QPSO)algorithm.Compared with the traditional particle swarm algorithm,the parameter setting of the QPSO algorithm is relatively simple,and the ability of particle search for the optimal solution is greatly improved.The paper analyzes the demodulation results of QPSO algorithm and adaptive mutation quantum particle swarm optimization(AMQPSO)algorithm,and compares the demodulation accuracy and time of the two algorithms.The demodulation error of AMQPSO algorithm in multi-FBG reuse system is not more than 3 pm,the accuracy of temperature measurement is up to 0.3℃,and the demodulation time is not more than 5 s.It solves the problem of partial overlap and complete overlap in multi-FBG system.