Brillouin spectrum denoising method based on wavelet transform

Abstract

The invention discloses a brillouin spectrum denoising method based on wavelet transform. The brillouin spectrum denoising method comprises the following steps of: S1, selecting a wavelet function with orthogonality, symmetry, compact support, urgent attenuation and smoothness; carrying out N-layer wavelet decomposition on a brillouin spectrum; S2, selecting a threshold value for a high-frequency coefficient at each layer; S3, carrying out threshold value quantization on the high-frequency coefficient at each layer; S4, carrying out lorentz curve fitting on a low-frequency coefficient at the N-th layer by adopting a nonlinear least square method; and S5, carrying out wavelet inverse transformation on the high-frequency coefficient at each layer, which is subjected to threshold value quantization and the low-frequency coefficient at the N-th layer, which is fitted. When the signal to noise ratio of the brillouin spectrum is small, the brillouin spectrum denoising method provided by the invention can obtain high-precision brillouin spectrum through less wavelet decomposition layers, and can effectively reduce the calculation quantity while effectively removing the noise.

Description

technical field [0001] The present invention relates to a denoising (namely denoising) method, in particular to a Brillouin spectrum denoising method based on wavelet transform. Background technique [0002] The Brillouin distributed optical fiber sensing technology can obtain the strain and temperature information along the optical fiber by analyzing the frequency shift of the Brillouin backscattering signal in the optical fiber. However, due to the influence of the external environment and system components, the measured Brillouin The spectrum is often noisy, which affects the fitting effect of the Brillouin spectrum, thereby affecting the detection accuracy of fiber stress and temperature. Therefore, denoising is required before fitting the Brillouin spectrum. [0003] At present, a feasible technical solution for denoising the Brillouin spectrum is as follows: (1) select a wavelet function and perform N-level wavelet decomposition on the Brillouin spectrum; (2) use the ...

Images