Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing

Abstract

Rapid data processing is crucial for distributed optical fiber vibration sensing systems based on a phase-sensitive optical time domain reflectometer (Φ-OTDR) due to the huge amount of continuously refreshed sensing data. The vibration sensing principle is analyzed to study the data flow of Rayleigh backscattered light among the different processing units. A field-programmable gate array (FPGA) is first chosen to synchronously implement pulse modulation, data acquisition and transmission in parallel. Due to the parallelism characteristics of numerous independent algorithm kernels, graphics processing units (GPU) can be used to execute the same computation instruction by the allocation of multiple threads. As a conventional data processing method for the sensing system, a differential accumulation algorithm using co-processing parallel computation is verified with a time of 1.6 μs spent of the GPU, which is 21,250 times faster than a central processing unit (CPU) for a 2020 m length of optical fiber. Moreover, the cooperation processes of the CPU and GPU are realized for the spectrum analysis, which could shorten substantially the time of fast Fourier transform analysis processing. The combination of FPGA, CPU and GPU can largely enhance the capacity of data acquisition and processing, and improve the real-time performance of distributed optical fiber vibration sensing systems.

Keywords

• distributed optical fiber sensor
• φ-otdr
• gpu
• parallel computation
• real-time performance