基于卡尔曼预测的光频域反射分布式光纤传感性能提升方法

Performance Enhancement Method of Optical Frequency Domain Reflection Distributed Fiber Sensing Based on Kalman Prediction

针对光频域反射(OFDR)分布式光纤传感在长距离、大量程应用场景中,参考光谱与测量光谱间的相似度(SD)退化及由此造成的鲁棒性下降的问题,本文研究了可调谐激光光源调谐非线性补偿模型,发现补偿的残余误差会引起传感单元产生随机位置偏差(PoD)。基于对PoD的统计学分析,建立了参考和测量光谱间SD的评价体系,并提出一种基于卡尔曼预测和局部寻优的传感单元随机PoD补偿方法,实现了参考和测量光谱位置的高效、精准匹配。本文所提方法能够在50 m的传感光纤上以5 mm的空间分辨率实现大量程传感(最高温度~450℃,最大应变~10000με),且兼顾高鲁棒性和高速度(计算量可降低到原来的5.8%~28.6%)。这些优点使该方法能够广泛应用于现有的光频域反射分布式光纤传感系统。

Objective Distributed fiber optic sensing technology based on optical frequency domain reflectance(OFDR)has found extensive application in areas such as monitoring the health of structures and measuring temperature/strain in harsh environments.It has proven advantageous due to its ability to provide high spatial resolution,compact design,lightweight nature,and excellent immunity to electromagnetic interference.However,since the backward Rayleigh scattered light used for localization in OFDR is usually weak,the reduction in similarity(SD)between the reference spectrum and the measurement spectrum due to noise can significantly impact the robustness and accuracy of the system's measurements,especially in situations involving long distances,high temperatures,or a significant number of range strains.To address this problem,in this paper,we develop a tuning nonlinearity compensation model for tunable laser sources,finding that the residual tuning nonlinearity may lead to a random position deviation(PoD)for each sensing gauge.Based on the PoD statistical analysis,we build a system for evaluating the SD between the reference and measurement spectra.Combining with Kalman prediction and local search,the proposed method can match the reference and measurement spectra efficiently and accurately,resulting in compensation for the random PoD introduced in the sensing gauge of interest.We hope to extend the sensing range while realizing increased spatial resolution,robustness,and speed.Methods The research on tuning nonlinearity starts from the schematic diagram of a polarization diversity OFDR system.By examining the origins of its residual tuning nonlinearities,we employ statistical techniques to explore how they impact the PoD in each sensing gauge.The analyses illustrate that the innate noise from the tunable laser,similar to the outer strain or temperature variations,could contribute to the PoD.In particular,because of the statistical portrayal of the residual tuning nonlinearities,the additionally generated PoDs exhibit an approximately standard distribution.Based on this finding,we further design a process based on Kalman filtering(KF)and local search to compensate for the random PoDs from tuning nonlinearities,wherein two judgment conditions(JC1 and JC2)determine whether to enter/break the local search loop.Compared with other post-filtering methods,this method updates the measurement information by satisfying JC1<T_(JC1) or minimizing JC2.This procedure is closer to real sensing scenarios and therefore improves the SD.Besides,we start the local search loop from the center(j=±1)with higher probabilities to the distal(j=±M)and break the loop once JC1<T_(JC1).Thus,the presented strategy could accelerate the search process.Results and Discussions We compare the distributed sensing results recovered by the proposed method with the existing methods(Fig.5).It is evident that the currently available approaches have limitations in terms of measurement length and strain/temperature measurement range due to the residual tuning nonlinearities.In contrast,the presented method can recover the strain/temperature distributed along the fiber axis without observing outliers,suggesting it can sufficiently compensate for the innate SD degradation due to the residual tuning nonlinearities.In particular,the robustness of the proposed method has a significant advantage when the measured strain or temperature is beyond 5000μεor 300℃,respectively.Additional examinations of the PoD random variations caused by the tuning nonlinearities and external stress indicate that the amplitude and range of the former are weaker(Fig.7),implying that it is typically confined and temporary.The requirement to implement the adaptive judgment conditions JC1 and JC2 is verified in parallel.The distributed fiber optic strain/temperature sensing equipment and its software can achieve a sensing distance of greater than 150 m and a spatial resolution of 5 mm(Fig.9),and the completion time of a single measurement under the full sensing range and the highest spatial resolution is less than 6 s.The system could measure strains varying from 2000 to 10000μεat about 140 m.A lateral comparison of each curve reveals that the shape of the data sets is similar,and the height of the"platform"is directly proportional to the applied strain.It is evident that the system effectively measures the magnitude and location of the sensing event;a horizontal comparison of the data sets demonstrates that the shape of the data sets is comparable,and the height of the"high platform"is linearly correlated to the applied strain.Conclusions In conclusion,the random PoD due to the residual tuning nonlinearities is theoretically verified to decrease the SD between the reference and measurement spectra in OFDR systems.A novel local search and dynamic prediction method based on KF is then proposed.This method can effectively compensate for the random PoD by local search and accelerate the search process by the KF prediction.Experiments show that the proposed method can significantly improve the robustness of the sensing system under the limited range(temperature of 450℃and strain of 10000με)sensing application.Moreover,it can compress the computation to 5.8%-28.6%of that without dynamic prediction operations.