基于模场自积增强检测的光纤声光旋转传感器

摘要

介绍了一种应变不敏感的基于模场自积增强检测的光纤声光旋转传感器.通过调节加载到光纤声致光栅上的微波频率能使双模光纤输出高纯度LP11模式.采用自积增强算法显著提高传感分辨比例,改善探测速度,实现对环境旋转角度变化的动态监测.传感器在0?—180?的测量范围内,角度最大测量误差范围小于11％;在轴向应变为100—1500με之间对应变不敏感.%Mechanical parameter monitoring based on optical mode detection benefits from its low cross sensitivity and inex-pensive instrument. The key to improving detection accuracy is to generate high-quality detection light and use efficient algorithms. We present a strain-independent torsion sensor based on acoustically-induced fiber grating (AIFG) in the dual-mode fiber (DMF) and use the enhanced self-integration algorithm to improve the sensing accuracy. By tuning the radio frequency of driving signal, the LP11 mode generated by the AIFG can be exploited to measure the dynamic torsion variations. Without the complex device such as fiber interferometers and photonic crystal fibers (PCFs), the simple structure built by mode converter and charge coupled device (CCD) can track the dynamic variations and has less cross sensitivity of strain along the transmission direction. The AIFG driven by a radio frequency as a mode converter at specific wavelength does not participate in sensing but generates the high-purity LP11 mode that accounts for more than 90％ of total power. With the twist from the rotator stage, the DMF keeps rotating and CCD records the spatial distribution of mode profiles. The features of optical mode is enhanced based on matrix analysis and then the relationship between twist angle and mode features is obtained. Based on image processing, the dynamic variation of spatial beam detected by CCD can be easily tracked and quantified. In experiment, the rotation angle can be obtained by calculating the feature value of the optical mode. Our image detection algorithm is specially designed for the optical fiber mode. Compared with traditional image recognition based on feature learning, it is simple and fast because it is needless to use image segmentation and stochastic processing. Through a series of experiments on angle rotation and parallel strain, we verify the correctness of the enhanced self-integration model and analyse the computational uncertainties that influence the stability of experiment. In the 0? to 180? measurement range, the maximum range of measurement error is less than 11％. When the axial strain is between 100 με and 1500 με, the sensor is strain-independent. Thus, it is verified that the torsion sensor based on AIFG has high sensitivity and can overcome the cross sensitivity of strain along a certain direction. The pertinent results have significant guidance in designing the multi-parameter sensor. The optical mode detection, instead of the traditional spectrum measurement, enables the whole structure to have the potential to be rebuilt by inexpensive devices that work in visible wavelengths.