Optical-pulse-coding phase-sensitive OTDR with mismatched filtering

Liang, Yongxin; Wang, Zinan; Lin, Shengtao; Wang, Yuyao; Jiang, Jialin; Qiu, Zijie; Liu, Chunye; Rao, Y. Yaswanth Naidu K. Ananda

doi:10.1007/s11432-021-3329-6

Cited by 12 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The processing of such data obtained by coherent reflectometric systems, in addition to the traditional fast discrete Fourier transform, in some cases includes the use of fairly simple software filters based on a moving differential or a moving averaging, as well as some nonlinear transformations [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Turov,

Barkov,

Konstantinov

et al. 2023

Algorithms

View full text Add to dashboard Cite

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms’ combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS.

show abstract

Section: Introductionmentioning

confidence: 99%

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Turov,

Barkov,

Konstantinov

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…Undoubtedly, this raises both the cost and complexity of the system. Simultaneously, Liang et al introduced mismatched filtering and phase coding into Φ-OTDR [19]. This technology mitigates the need for a high degree of randomness in the coding sequence, making it easy to achieve a good PSL.…”

mentioning

confidence: 99%

Pulse Intensity Coding Phase-Sensitive OTDR With Mismatched Filtering

Xie,

Xu,

Cao

et al. 2024

IEEE Photonics J.

View full text Add to dashboard Cite

Optical pulse coding phase-sensitive optical timedomain reflectometry (Φ-OTDR) sends a series of pulses into the sensing fiber to enhance signal-to-noise ratio (SNR). And the spatial resolution is determined by the subpulse duration. The current challenges in optical pulse coding Φ-OTDR include long measurement time, complex coding design, and intricate modulation process. In this paper, pulse intensity coding and mismatched filtering were introduced, which could reach the theoretical measurement time determined by the fiber length. Additionally, it also simplified coding design and modulation process. In order to obtain satisfactory decoding results, the least-squares criterion was used to design the mismatched filter. Experimental result demonstrates a 6.6 dB improvement in the SNR of the demodulated signal using a 10-bit coding pulse.

show abstract