基于下变频和IQ解调的外差型相位敏感光时域反射技术的模式识别

Pattern Recognition of Heterodyne Phase-Sensitive Optical Time-Domain Reflection Technique Based on Down Conversion and IQ Demodulation

基于模拟下变频器、数字IQ解调和反向传播(BP)神经网络,采用现场可编程门阵列结合数字信号处理器(FPGA+DSP)的数据采集和处理架构,提出了一种全嵌入式高信噪比(SNR)、高分辨率和低成本的外差型相位敏感光时域反射(Φ-OTDR)技术模式识别方法。针对外差型Φ-OTDR技术,使用DSP、FPGA及其外围硬件电路替代原有的GHz级高速采集卡和信号发生器,减小了系统的体积和成本。在此基础上,设计了基于时空域二维图提取形态学特征的方法,并采用BP神经网络进行分类识别;所提方法相对于传统的针对一维信号进行模式识别的方法误报率更低、识别率更高。实验结果表明,所设计的基于FPGA+DSP全嵌入式并行信号处理架构满足实时监测的要求,SNR高达12.43dB,事件识别准确率达到97.78%。

An embedded pattern recognition method for heterodyne phase-sensitive optical time-domain recognition（Φ-OTDR）technique with high signal-to-noise ratio（SNR）,high resolution and low cost is proposed based on analog down conversion,digital IQ demodulation and back propagation（BP）neural network.When we use a digital signal processor（DSP）,field programmable gate array（FPGA）and a peripheral circuit to replace GHz data acquisition and signal generator,cost and size are reduced.A method based on time and space two-dimensional extracting morphological features is designed,and the BP neural network is used to multi-class recognition.Compared with the traditional mode recognition for one-dimensional signal,the proposed method can achieve lower false alarm rate and higher recognition rate.Experiment results show that designed embedded parallel signal processing architecture based on FPGA＋DSP can satisfy the real-time monitoring requirements.SNR of the system is 12.43 dB and the event recognition rate is 97.78%.