Brillouin optical time domain analyzer (BOTDA) fiber sensors have shown strong capability in static long haul distributed temperature/strain sensing. However, in applications such as structural health monitoring and leakage detection, real-time measurement is quite necessary. The measurement time of temperature/strain in a BOTDA system includes data acquisition time and post-processing time. In this article, we propose to use hardware accelerated support vector regression (SVR) for the post-processing of the collected BOTDA data. Ideal Lorentzian curves under different temperatures with different linewidths are used to train the SVR model to determine the linear SVR decision function. The performances of SVR are evaluated under different signal-to-noise ratios (SNRs) experimentally. After the model coefficients are determined, algorithm-specific hardware accelerators based on field-programmable gate arrays (FPGAs) are used to realize SVR decision function. During the implementation, hardware optimization techniques based on loop dependence analysis and batch processing are proposed to reduce the execution latency. Our FPGA implementations can achieve up to 42× speedup compared with software implementation on an i7-5960x computer. The post-processing time for 96 100 Brillouin gain spectrums (BGSs) along with 38.44-km fiber under test (FUT) is only 0.46 s with FPGA board ZCU104, making the post-processing time no longer a limiting factor for dynamic sensing. Moreover, the energy efficiency of our FPGA implementation can reach up to 226.1× higher than the software implementation based on CPU.
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - Jun 2020|