Signal processing methods of phase sensitive optical time domain reflectometer：a review

Man-ling TIAN; Dong-hui LIU; Xiao-min CAO; Kuang-lu YU

doi:10.37188/OPE.20212909.2189

Optics and Precision Engineering, Volume. 29, Issue 9, 2189(2021)

Signal processing methods of phase sensitive optical time domain reflectometer：a review

Man-ling TIAN1...2,*, Dong-hui LIU1,2, Xiao-min CAO1,2, and Kuang-lu YU12,* |Show fewer author(s)

Author Affiliations

¹Institute of Information Science， Beijing Jiaotong University， Beijing00044， China

²Beijing Key Laboratory of Modern Information Science and Network Technology， Beijing100044， China

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Figures & Tables(13)

Fig. 1. Ф-OTDR system

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Fig. 2. Schematic diagram of wavelet decomposition （solid line） and wavelet packet decomposition （solid line plus dashed line）

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Fig. 3. Threshold function in wavelet denoising

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Fig. 4. EMD decomposition （IMFL： the signal after the decomposition of the L layer； resL： the residual component after the decomposition of the Lth layer）

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Fig. 5. Signal reconstruction process

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Fig. 6. ANN network structure with single hidden layers

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Fig. 7. SVM structure

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Fig. 8. Combination of multiple classifiers

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Fig. 9. CNN-based Ф-OTDR data processing scheme

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Fig. 10. LSTM-based Ф-OTDR data processing scheme

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Table 1. Literature summary of feature extraction methods

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Table 1. Literature summary of feature extraction methods

特征提取类别		特征提取方法
^［19］	时域	阈值判断、均值
^［58］	时域	归一化
^［22］	频域	STFT
^［24］		归一化
^［37］		STE、水平交叉率（LCR）、Welch法
^［59］	时域+频域	快速傅里叶变换（FFT）
^［57］		归一化、差分法、WPD、奇异值分解
^［26］		直方图、WPD、数理统计
^［25］		WPD、ZCR、STE、经验模态分解
^［28］		Welch法、周期图法
^［38］		梅尔倒谱系（MFCC）、短时过零率、ST、ZCR、短时能量（STE）
^［20］ ^［56］ ^［21］	时频域	多尺度小波分解 ST-FFT 小波分解、小波包分解
^［60］		频谱图图像特征（SIF）
^［30］		小波能量谱分析
^［23］		STE、LCR、功率谱分析、WD
^［61］		经验模态分解
^［29］	其他	图像处理

Table 2. Common time domain characteristics table

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Table 2. Common time domain characteristics table

特征	定义
峰值（PK）	$X_{P K} = m a x (X)$
最小值（Min）	$X_{M i n} = m i n (X)$
峰值差（PK-PK）	$X_{P K - P K} = \frac{[m a x (X) - m i n (X)]}{2}$
能量（E）	$X_{E} = 10 l g [\sum_{i = 1}^{n} {(x_{i} - \bar{X})}^{2}]$
均值（Mean）	$\bar{X} = \frac{1}{n} \sum_{i = 1}^{n} x_{i}$
平均整流值（Arv）	$X_{A r v} = \frac{1}{n} \sum_{i = 1}^{n} \| x_{i} \|$
均方根（RMS）	$X_{R M S} = \sqrt[]{\frac{1}{n} \sum_{i = 1}^{n} x_{i}^{2}}$
方差（var）	$X_{v a r} = \frac{1}{n} \sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}$
标准差（SD）	$X_{S D} = \sqrt[]{\frac{1}{n} \sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}}$

Table 3. Classification algorithm performance comparison

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Table 3. Classification algorithm performance comparison

文献	分类算法	传感距离/km	分类准确率	事件种类	误报率	识别时间/s
^［65］	ANN		95%	3
^［20］	ANN		89.19%	3	1.75%
^［21］	ANN	65	94.4%	3	6.7%
^［29］	RVM	20	97.8%	3		<1
^［30］	RVM	10	88.6%	3
^［23］	SVM	20	93.8%	5
^［24］	二叉树结构的SVM	25.05	>94%	4	<4%
^［19］	改进SVM		95.72%	3
^［27］	NC-SVM	25.05	94.3%	5	5.62%	0.55
^［56］	GMM	45	80%	2
^［22］	GMM	45	69.7%	8	31.2%
^［59］	RF		98.67%	2
^［58］	RF		96.58%	4
^［61］	XGBoost	25.05	95.9%	5	4.1%	0.093
^［60］	ELM	10	93.9%	5		1.070 6
^［57］	F-ELM	25.05	95.3%	5	4.67%	<0.1
^［26］	SVM+BPNN+贝叶斯网络	65	98%	2	0%
^［25］	SVM（Ⅰ级分类器） RF/GBDT（Ⅱ级分类器）	10	99.41% 98.02%/97.89%	2 4	0.69%
^［28］	SVM+BPNN+AdaBoost	25.05	94.1%	5
^［31］	2D CNN+SVM	40	93.3%	4		0.6
^［32］	2D CNN		98.02%
^［33］	2D CNN （改进的Googlenet）	1	96.67%	5
^［74］	2D CNN+SVM	1	94.17%	8
^［75］	2D CNN	2	>98%	5
^［34］	1D CNN	34	95.55%	5
^［35］	MS 1-D CNN	40	96.59%	3
^［77］	MS CNN		84.67%	6		0.017
^［76］	AF-CNN		96.7%	4
^［38］	ALSTM	50	94.3%	5		0.91
^［80］	CLDNN	33	97.2%	3	8%
^［81］	ConvLSTM	40	85.6%	3		8.25
^［82］	3D CNN+ConvLSTM	40	>90%	3	~10%
^［83］	MLSTM-CNN	25.05	95.7%	5	4.3%	1.20
^［84］	1D-CNNs+Bi LSTM	40	97%	5

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Man-ling TIAN, Dong-hui LIU, Xiao-min CAO, Kuang-lu YU. Signal processing methods of phase sensitive optical time domain reflectometer：a review[J]. Optics and Precision Engineering, 2021, 29(9): 2189

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