利用光纤布拉格光栅(FBG)构建大型传感网络时,在相同带宽的条件下,为了增加传感器的数量,会尽可能多地写入光纤光栅,因此将导致部分光谱发生重叠,使FBG中心波长识别困难,解调精度降低。为此,提出一种改进粒子群优化(PSO)算法,以提高中心波长的识别精度。结合光谱形状复用技术构建重叠光谱模型,搭建温度实验系统来获取重叠光谱信号,并对PSO算法中的权重因子和学习因子进行了改进,利用所提算法对重叠光谱模型参数进行优化,并将其与6种优化算法进行对比。仿真与实验结果表明,所提算法与对比算法相比,具有收敛速度快、运行时间短、波长识别精度高的特点,且波长解调误差均小于1 pm,验证了算法的有效性和可行性。

When we construct a large distributed sensing network by fiber Bragg grating (FBG), under the condition of same bandwidth, we fabricate as many fiber gratings as possible to increase the number of sensors, which leads to overlapping spectra and makes FBG central wavelength recognition difficult and reduces the demodulation accuracy. In order to solve this problem, we propose an improved particle swarm optimization (PSO) algorithm to improve the recognition accuracy of the central wavelength. First, the overlapping spectra model is established by spectral shape multiplexing technology. Then, the temperature experiment system is built to get the overlapping spectral signals, and the weight factor and the learning factor in the PSO algorithm are improved. Finally, the proposed algorithm is used to optimize the parameters of the overlapping spectra model, and it is compared with the six optimization algorithms. Simulation result and experimental result show that the proposed algorithm has the characteristics of fast convergence speed, short running time and high wavelength recognition accuracy compared with the contrast algorithm, and the wavelength demodulation error is less than 1 pm, which verifies the effectiveness and feasibility of the algorithm.