面向带宽重叠复用的光纤光栅传感网络非对称光谱解调研究

Fiber Bragg grating (FBG) sensor network has been the most promising optical fiber sensing technique owing to its advantageous properties such as linear sensing, highly sensitive and anti-electromagnetic interference. To address the problems caused by asymmetric spectra and the low bandwidth efficiency in the practical FBG sensor network, this project attempts to propose a novel demodulation technique for spectral bandwidth overlapped multiplexing FBG sensor network. In the project, we will analyze the features of the actual multi -peak asymmetric spectra. The spectral data in various states will be utilized to complete clustering and reconstruct the spectral model. Multiple FBG spectral optimization model under complex constraints will be established, and intelligent wavelength detection algorithm will be designed based on the frame of distributed estimation algorithm. By integrating the correlation between demodulation performance and multiplexing performance, the bandwidth resource allocation scheme based on the probability distribution will be proposed to enhance bandwidth resource utilization and the multiplexing capabilities of FBG sensor network. We will evaluate levels of demodulation and multiplexing performance under noise environment. The noise restraint of the model will be enhanced to improve the integrated performance of FBG network. The project aims to deepen the applications of intelligent optimization methods in FBG sensing field. This study will enrich and develop demodulation of sensor signals, and provide the theoretical references and technical supports for high-capacity and high-accuracy FBG sensor network.

光纤光栅（FBG）传感网络凭借其线性传感、高灵敏度、抗电磁干扰等优良特性，是目前最具潜力的光传感技术。针对FBG传感网络在实用化过程中存在的非对称光谱解调难和带宽资源利用率低两大问题，本项目力图在带宽重叠复用模式下为FBG的高精度传感解调提供一种全新的解决方案：分析FBG实际光谱的多峰非对称特性，完成不同状态下非对称谱数据的聚类辨识和光谱重构；建立复杂约束下多FBG光谱形态的参数辨识优化模型，设计基于分布式估计算法框架下的智能波长检测算法，实现非对称谱的高速解调；结合带宽重叠下解调性能和复用性能的耦合相关性，构建基于概率分布的带宽资源分配模型，优化带宽资源配置，提高复用容量；评估系统噪声环境下的解调复用水平，改进模型抗噪特性，提升网络综合性能。本项目旨在深化智能优化技术在FBG传感领域的应用，以期丰富和发展光传感信号解调手段，为实现大容量、高精度的智能FBG传感网络提供理论参考和技术支撑。

光纤光栅（FBG）传感网络凭借其线性传感、高灵敏度、抗电磁干扰等优良特性，是目前最具潜力的光传感技术。针对FBG传感网络在实用化过程中存在的非对称光谱解调难和带宽资源利用率低两大问题，本项目对带宽重叠复用模式下FBG传感网络的高精度传感解调展开研究，获得了以下研究成果：对FBG传感网络的不同类型非对称光谱形态特征进行辨识和分析，并引入非对称参数构建了FBG光谱重构理论模型；完成了基于多参量辨识的FBG传感网络非对称重叠光谱解调优化模型的建模与求解，设计基于分布式估计算法框架下的智能波长检测算法，实现了非对称谱的快速解调；考虑光谱重叠程度与网络带宽资源，设计基于概率分析的带宽资源分配模型，在保证波长高精度解调下实现了带宽资源利用率的提升；结合仿真和实验手段完成FBG传感网络的性能评估，并通过改进优化算法提高了传感网络解调和复用水平。本项目的研究成果促进了智能优化技术在FBG传感领域的应用，为实现大容量、高精度的智能FBG传感网络提供理论参考和技术支撑。