Abstract
The technology to improve the spatial resolution of distributed sensing has always been a research hotspot. In this paper, a ROTDR (Raman-OTDR) system with a variable sampling rate and a spatial resolution optimization algorithm are designed, as shown in the figure, which makes the distribution of sampling points dense and improves spatial resolution. A point selection matrix based on the reconstruction algorithm is generated to reconstruct a one-dimensional high-precision signal. Furthermore, an optimization algorithm based on the reconstruction algorithm is designed, which can select the sampling rate and flexibly set the desired spatial resolution. Sampling rate combination and offset distance are set as the output of this algorithm to improve the response time and eliminate the blind area in the reconstruction process. The simulation results show that the reconstruction algorithm can improve the spatial resolution of the distributed temperature measurement system to 0.25 m at the cost of sampling ten times at different sampling rates (91 MHZ - 100 MHZ), representing a resolution gain of four times compared with the spatial resolution limited to 1 m by the acquisition card. This paper also compares the sampling results at a single sampling rate, random sampling rate combination, and optimized sampling rate combination to certify the effectiveness of this technology. The sampling result at an optimized sampling rate combination has the highest measurement accuracy.
Published Version
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