Characterization of DWT as Denoising Method for ?-OTDR Signal
DAS system based on ?-OTDR technique suffers from random noises that affect the signalto-noise-ratio of the extracted signals. This results in high false alarm rate, reducing the capabilities of the systems to detect vibration signals. This paper presented a thorough analysis of a denoising method u...
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| 主要な著者: | , , , , , , , , |
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| フォーマット: | 論文 |
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Universiti Malaysia Perlis
2023
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| 要約: | DAS system based on ?-OTDR technique suffers from random noises that affect the signalto-noise-ratio of the extracted signals. This results in high false alarm rate, reducing the capabilities of the systems to detect vibration signals. This paper presented a thorough analysis of a denoising method using discrete wavelet function (DWT). We implemented and compared different mother wavelets such as Symlet 4, Haar, Daubechies 4 (Db4), Biorthogonal 4.4 (Bior4.4), Coiflets 3 (Coif3), Discrete approximation of Meyer wavelet (dmey), Fej�r-Korovkin filters 8 (fk8) and Reverse Biorthogonal 6.8 (rbio6.8), using multiple levels of decomposition. Four denoising thresholds, Empirical Bayes, Universal Threshold, Stein's Unbiased Risk Estimation (SURE), and Minimax Estimation (Minimax) were characterized using soft threshold rule. From the results obtained, the combination of the Daubechies 4 wavelet function, level 3 decomposition, SURE denoising threshold with soft threshold rule produces the best denoising performance on the ?-OTDR data. � 2021, Universiti Malaysia Perlis. All rights reserved. |
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