The Research of Optical Fiber Brillouin Spectrum Denoising Based on Wavelet Transform and Neural Network

The center frequency of Brillouin scattering spectrum is easily influenced by the noise and the measurement accuracy of optical fiber strain is reduced. So a novel denoising method which can be applied in the Brillouin scattering spectrum is developed in this article. The Brillouin scattering spectrum is decomposed into multi-scale detail coefficients and approximation coefficients by using the wavelet transform. The wavelet decomposition detail coefficients are threshold quantified by utilizing the threshold algorithm. At the same time, the wavelet decomposition approximation coefficients are trained and simulated by using the BP neural network in order to remove noise hided in the approximation coefficients. So the novel method can reduce the wavelet decomposition scales. The Brillouin scattering spectrum which has a better denoising effect can be gained by using the inverse wavelet transform, and the measurement accuracy of optical fiber strain is enhanced also. The results of simulation and experiment demonstrate that the proposed method can suppress noise better; accordingly, the new method can gain more precision optical fiber strain and reduce the wavelet decomposition scales effectively than the conventional wavelet denoising method. Theory analysis and experiment show that the method is reasonable and efficient.