Segmented Noise Reduction Based on Brillouin-Spectrum-Partition in Brillouin Optical Time Domain Sensors

Zhang, Yuyang; Lu, Yuangang; Chen, Liang; Wu, Tong; He, Chongjun

doi:10.1109/jsen.2021.3110205

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…The AM n change caused by each NLM parameter is 9.55%, 0.05%, and −0.04%, respectively, which indicates that the decrease in AM is mainly caused by the GSP. It is worth noting that if all the NLM parameters are optimal, the FWHM of the denoised trace only expands by only 2.56% compared with that of the clean trace, which is much less than the previous results [13,14,19,20,22]. The total signal amplitude is reduced by 9.25% compared with the clean trace.…”

Section: Discussionmentioning

confidence: 61%

“…The traces denoised with three SEWS values (20,251,260) are almost identical to the clean trace (blue line) when GSP = 0.052 and SIWS = 14, as shown in Figure 4i. Obviously, the SEWS = 260 has no denoising effect on the noisy trace.…”

Section: Fwhm Varies With Nlm Parametersmentioning

confidence: 68%

“…where NLM SR represents the SR of the signal denoised by NLM, the corresponding SR states the SR of the signal obtained from the AAD [14], the ideal data [19], the Gaussian filtered results [20], and the data averaged over 5000 measurements [22], respectively. Compared to the corresponding SR of the AAD, Gaussian filtering, and the average of 5000 measurements, the SR degradation percentages caused by NLM are 93%, 275%, 84%, and 8.97%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have shown that the SR for ideal data and original noisy data is 0.4 m, and it retains 0.4 m by Brillouin spectrum-reassembly filter, but the SR deteriorates to 1.5 m by NLM filter [19]. The combination of NLM and Gaussian filter can effectively improve the performance of the distributed Brillouin fiber sensor compared to the results obtained with NLM, enabling a 22% improvement in MA in the non-transition section and an 84% improvement in SR in the transition section [20]. Anisotropic diffusion provides better measured MA and SR than the other three methods (wavelet denoising, NLM, Block Matching and 3D filtering), but worsens the root mean square error (RMSE) in experiments [13].…”

Section: Introductionmentioning

confidence: 95%

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Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Chen

Zhu

et al. 2023

Photonics

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Non-local mean (NLM) can significantly improve the signal-to-noise ratio (SNR), but it inevitably reduces the spatial resolution of distributed optical fiber sensors (DFOS), which hinders its practical application and the improvement of DFOS performance. In this paper, the quantitative relationship between the signal broadening of a phase-sensitive optical time-domain reflectometer (φ-OTDR) and the NLM parameters is analyzed to identify the cause and extent of the spatial resolution degradation. The denoising results for the mimic periodic and φ-OTDR vibration signals indicate that the signal broadening is mainly due to the similarity window size of NLM, and the signal amplitude reduction is caused by the Gaussian smoothing parameter. Compared with the reference signals, the signal broadening of the mimic and φ-OTDR signals after denoising are 2.56% and 2.74%, respectively, which is much less than the previous results. The signal amplitude is reduced by 9.25% and 13.62%, respectively. This work promotes the application of NLM and improves the performance of DFOS.

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Section: Discussionmentioning

confidence: 61%

Section: Fwhm Varies With Nlm Parametersmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

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Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Chen

Zhu

et al. 2023

Photonics

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“…Various techniques have been proposed to improve the performance of distributed Brillouin fiber sensors [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Among these advanced techniques, methods such as optical pulse coding [26][27][28], distributed Raman amplification [29][30][31], and different signal processing methods [32][33][34][35] exhibit better performances than classical standard configurations.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement in a Few-Mode Rayleigh-Brillouin Optical Time Domain Analysis System Using Pulse Coding and LMD Algorithm

Zhang,

Li,

Wang

et al. 2024

Photonics

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Rayleigh Brillouin optical time domain analysis (BOTDA) uses the backscattered Rayleigh light generated in the fiber as the probe light, which has a lower detection light intensity compared to the BOTDA technique. As a result, its temperature-sensing technology suffers from a low signal-to-noise ratio (SNR) and severe sensing unreliability due to the influence of the low probe signal and high noise level. The pulse coding and LMD denoising method are applied to enhance the performance of the Brillouin frequency shift detection and temperature measurement. In this study, the mechanism of Rayleigh BOTDA based on a few-mode fiber (FMF) is investigated, the principles of the Golay code and local mean decomposition (LMD) algorithm are analyzed, and the experimental setup of the Rayleigh BOTDA system using an FMF is constructed to analyze the performance of the sensing system. Compared with a single pulse of 50 ns, the 32-bit Golay coding with a pulse width of 10 ns improves the spatial resolution to 1 m. Further enhanced by the LMD algorithm, the SNR and temperature measurement accuracy are increased by 5.5 dB and 1.05 °C, respectively. Finally, a spatial resolution of 1.12 m and a temperature measurement accuracy of 2.85 °C are achieved using a two-mode fiber with a length of 1 km.

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