A Dual-Adaptive Denoising Algorithm for Brillouin Optical Time Domain Analysis Sensor

Zhang, Qian; Wang, Tao; Zhao, Jieru; Liu, Jingyang; Wang, Yahui; Zhang, Jianzhong; Qiao, Lingling; Zhang, Mingjiang

doi:10.1109/jsen.2021.3105191

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…The local mean decomposition (LMD) method is a time-frequency signal decomposition technique that progressively separates frequency-modulated signals from amplitudemodulated envelope signals [54,58]. LMD can decompose the amplitude-and frequencymodulated signals into product function (PF) components, with each product function being the product of an envelope signal and a frequency-modulated signal, from which the time-varying instantaneous phase and instantaneous frequency can be derived.…”

Section: Lmd Methodsmentioning

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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Section: Lmd Methodsmentioning

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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“…These algorithms have achieved good results in the fields of image segmentation, image recognition, and image fusion. Zhang et al 32 . proposed a dual adaptive denoising algorithm based on complementary integrated empirical modal decomposition and PSO to improve the signal-to-noise ratio of the Brillouin optical time-domain analysis sensor.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms have achieved good results in the fields of image segmentation, image recognition, and image fusion. Zhang et al 32 proposed a dual adaptive denoising algorithm based on complementary integrated empirical modal decomposition and PSO to improve the signal-to-noise ratio of the Brillouin optical time-domain analysis sensor. Kong et al 33 proposed a chaotic dynamic weighted particle swarm adaptive wavelet threshold denoising method based on sigmoid acceleration coefficients to adaptively eliminate noise and retain the underlying fault characteristic signal of wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.

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“…Recently, digital signal processing (DSP) methods without any hardware modification became the focus of the research on the SNR improvement. One-dimensional digital denoising methods, such as the wavelet (packet) denoising (WD) 21 and empirical mode decomposition, 22 , 23 have been proposed, but the denoising performance of these methods is greatly affected by the selection of wavelet basis functions or modal aliasing. Machine learning technology is also proposed for denoising 24 .…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio enhancement using block-matching and 3D filtering for both Brillouin gain and phase spectra in sweep-free Brillouin optical time-domain analyzer

Zhao

Zeng

et al. 2022

Opt. Eng.

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. Conventional Brillouin optical time-domain analyzer (BOTDA) requires the process of frequency scanning, which is very time-consuming. The data acquisition speed of the conventional BOTDA is too slow to realize dynamic sensing. The use of the optical frequency comb-based BOTDA can avoid the frequency scanning and speed up the measurement. But, to achieve dynamic measurement, it is also necessary to reduce the time for signal averaging to further improve the measurement speed. However, few averaging times lead to poor signal-to-noise ratio (SNR), which will limit the sensing performance of the system. Block-matching and 3D filtering (BM3D) algorithm is proposed to achieve the denoising of sensing signals in the sweep-free BOTDA using intensity-modulated direct-detection optical orthogonal frequency division multiplexing. The impact of the BM3D parameters on the denoising performance is analyzed, which is used to realize parameter optimization. To demonstrate the denoising performance of BM3D under different noise levels, white Gaussian noise with different standard deviation (STD) is added to the original sensing signal. When the STD of the added noise is 0.07 mV, the SNR after denoising using optimized BM3D is improved by 9.7 dB for the Brillouin gain spectrum (BGS) and 10.6 dB for the Brillouin phase spectrum (BPS), respectively, and the frequency uncertainty is reduced to be below 1 MHz. For a wide range of noise levels, BM3D has shown excellent denoising performance, and the average SNR after denoising is improved to be 16.8 and 15.6 dB for BGS and BPS, respectively, and the corresponding frequency uncertainty is reduced to be below 1.2 MHz.

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A Dual-Adaptive Denoising Algorithm for Brillouin Optical Time Domain Analysis Sensor

Cited by 8 publications

References 33 publications

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

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

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Signal-to-noise ratio enhancement using block-matching and 3D filtering for both Brillouin gain and phase spectra in sweep-free Brillouin optical time-domain analyzer

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