Extracting Brillouin frequency shift accurately based on particle swarm optimization and a cross-correlation method.

A fitting algorithm based on particle swarm optimization (PSO) and the cross-correlation method (XCM) was proposed to accurately extract features of the Brillouin scattering spectrum. Both the simulation and experiment were conducted to verify the validity of the proposed algorithm. The results show that the algorithm achieves high demodulation accuracy and is suitable for Brillouin scattering spectra with different signal-to-noise ratios, full width at half-maximums, frequency sweep intervals, and spectral symmetries. Compared with the Lorenz curve fitting and XCM algorithm, the extraction error of the PSO-XCM can be optimized up to 99.98% and 99.93%, and the fitting degree can be improved by 98%. Moreover, based on the proposed algorithm, the minimum temperature measurement error can reach 0.06°C and the Brillouin frequency shift measurement error is 0.07 MHz in 10 km sensing fiber for the Brillouin optical time domain analysis (BOTDA) sensing system.