【摘要】 为实现远程光纤传感网络在噪声干扰条件下的高精度检测传输，提出一种优化差分(optimized difference,OD)的降噪方法。基于移动平均法构建新差分算法，并结合低通算法、中值算法预判边缘信息及预测信号数值；采用双重判别弥补边缘信息误判的缺陷，优化准确定位边缘信息的能力；融合加权平均法与卡尔曼滤波器(Kalman filter,KF)，优化信号预测的精度。实验验证结果表明：采用该方法进行数据预处理将使系统能够正常检测数据，测量精度可达0.76%，提高检测系统的抗噪性能。与先进小波阈值降噪方法的对比实验结果表明：斜率优化方法在≥10 dB噪声干扰下，测量精度相对提高0.62倍以上。更多还原

【Abstract】 In order to achieve high-precision detection and transmission of remote fiber optic sensing networks under noise interference conditions, an optimized differential(OD) denoising method is proposed. Construct a new differential algorithm based on the moving average method, and combine low-pass algorithm and median algorithm to predict edge information and signal values; Using double discrimination to compensate for the shortcomings of edge information misjudgment and optimize the ability to accurately locate edge information; Combining weighted average method and Kalman filter(KF), the accuracy of signal prediction is optimized. The experimental verification results show that using this method for data preprocessing will enable the system to detect data normally, with a measurement accuracy of 0.76%,and improve the anti noise performance of the detection system. The comparative experimental results with an advanced wavelet threshold denoising method show that the slope optimization method can improve the measurement accuracy by more than 0.62 times under ≥10 dB noise interference.更多还原