Image-deconvoluted random coding BOTDR

Brillouin optical time domain reflectometry (BOTDR) inherently involves a trade-off between sensing range and spatial resolution, particularly in long-distance applications. This paper presents an image-deconvoluted random coding BOTDR approach to simultaneously enhance both sensing range and spatial resolution. In this method, a series of pulses modulated with random codes is employed to extend the sensing range, while the Richardson-Lucy image deconvolution algorithm is applied to refine spatial resolution. The decoding principle for random pulse detection BOTDR is systematically described, and the corresponding point spread function (PSF) is constructed. Numerical simulations are conducted to optimize coding parameters, achieving a simulated spatial resolution of 1 m over a 100 km sensing fiber. The proposed method is experimentally implemented and thoroughly evaluated. The experiment results demonstrate that, without any hardware modifications, the combination of 256-bit random coding with 100 ns single-code width and image deconvolution extends the sensing range of BOTDR to 100.4 km with a root-mean-square error (RMSE) below 2 MHz, while achieving an actual spatial resolution of 2 m at the fiber end. These findings indicate that the proposed approach can significantly improve both the sensing range and spatial resolution in BOTDR sensing systems.