提升复杂环境下Φ-OTDR的干扰信号识别：有效时间分离与确定策略

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本文针对相位敏感光时域反射仪（Phase-Sensitive Optical Time Domain Reflectometry, Φ-OTDR）在复杂噪声环境中面临的挑战，提出了一种有效的时间信号分离和确定方法。Φ-OTDR是一种广泛应用于光纤通信系统中用于测量光纤链路性能的精密仪器，然而其对环境噪声的高度敏感性常常导致难以精确识别真实的人为入侵，进而引发高噪声报警率（Noise Alarm Rates, NARs），影响其在实际应用中的性能。传统的Φ-OTDR分析侧重于横向空间信号处理，这种方法在面对复杂的随机干扰时显得力不从心。作者创新地采用了一种时间演化信号处理策略。首先，通过对不同时间点上OTDR轨迹的累积，每个空间点获取到其动态变化的时域信号。这种方法提高了对瞬息万变的信号动态的捕捉能力。接着，作者引入了多尺度小波分解技术来分析这些纵向时间信号。小波分析的优势在于其局部化特性，能够对信号进行多尺度的精细分解，将时域信号拆解为一系列具有不同频率成分的子信号。通过选择合适的基函数，可以有效地分离出干扰信号与信号本身的贡献，使得信号特征得以清晰展现。这种方法的关键步骤包括：选择适当的多尺度小波函数，设置合适的分解层次，以及利用阈值处理来区分有用信号和噪声。通过对分解后的信号进行重构和后处理，可以进一步提取出干扰信号的特性，并确定它们的存在位置和强度。这有助于降低误报率，提高检测精度，从而显著改善Φ-OTDR在嘈杂环境下的性能。该研究为解决Φ-OTDR在复杂噪声背景下的性能优化问题提供了一个新的思路，通过结合时间演化信号处理和小波分析，有望提升设备在实际应用中的鲁棒性和可靠性，对于提升光纤通信系统的安全性具有重要意义。

3156 JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 33, NO. 15, AUGUST 2015

Separation and Determination of the Disturbing

Signals in Phase-Sensitive Optical Time Domain

Reﬂectometry (

Φ-OTDR)

Huijuan Wu, Member, IEEE, Member, OSA, Shunkun Xiao, Xiaoyu Li, Zinan Wang, Member, IEEE, Member, OSA,

Jiwei Xu, and Yunjiang Rao,Member,OSA

Abstract—Phase-sensitiveoptical time domain reﬂectometry (Φ-

OTDR) is easy to be interfered by ambient noises, and the non-

linear coherent addition of different interferences always makes

it difﬁcult to detect real human intrusions and causes high nui-

sance alarm rates (NARs) in practical applications. In this paper,

an effective temporal signal separation and determination method

is proposed to improve its detection performance in complicated

noisy environments. Unlike the conventional analysis of transverse

spatial signals, the time-evolving sensing signal of Φ-OTDR sys-

tem is at ﬁrst obtained for each spatial point by accumulating the

changing OTDR traces at different moments. Then, its longitudi-

nal temporal signal is decomposed and analyzed by a multi-scale

wavelet decomposition method. By selectively recombining the cor-

responding scale components, it can effectively extract human in-

trusion signals, and separate the inﬂuences of slow change of the

system and other environmental interferences. Compared with the

conventional differentiation way and fast Fourier transformation

denoising method, the SNRs of the detecting signals for the pro-

posed method is always the best, which can be raised by up to

∼35 dB for the best case. Moreover, from the decomposed compo-

nents, different event signals can be effectively determined by their

energy distribution features, and the NAR can be controlled to be

less than 2% in the test.

Index Terms—Detection, distributed optical ﬁber sensing,

extraction, Φ-OTDR, signal separation.

I. INTRODUCTION

HASE-SENSITIVE optical time-domain reﬂectometry

(Φ-OTDR) system [1]–[3], is a typical distributed ﬁber-

optic sensing technology to detect and locate multiple weak

vibration along the sensing ﬁber. It provides a cost-effective and

highly sensitive intrusion detection method for long distance

Manuscript received September 1, 2014; revised January 27, 2015; accepted

March 31, 2015. Date of publication April 9, 2015; date of current version June

20, 2015. This work was supported by the Natural Science Foundation of China

under Grants 61290312 and 61301275, the Fundamental Research Funds for

the Central Universities under Grant ZYGX2011J010, and also supported by

Program for Changjiang Scholars and Innovative Research Team in University

(PCSIRT, IRT1218), and the 111 Project (B14039).

H. Wu is with the Key Lab of Optical Fiber Sensing and Communications

(Ministry of Education), Center for Information in BioMedicine, University

of Electronic Science and Technology of China, Chengdu, Sichuan 611731,

China (e-mail: hjwu@uestc.edu.cn).

S. Xiao, X. Li, Z. Wang, J. Xu, and Y. Rao are with the Key Lab of Opti-

cal Fiber Sensing and Communications (Ministry of Education), University of

Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.

Color versions of one or more of the ﬁgures in this paper are available online

at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/JLT.2015.2421953

perimeter security, oil or gas pipe and large structure safety

monitoring and many other applications. Thus the system has

been extensively explored [2]–[6], and many key parameters

such as sensing length, spatial resolution, frequency response

range, etc., have already been signiﬁcantly improved in recent

years [7]–[10]. However, for Φ-OTDR systems, the disturbances

that are not threatening could cause the same disturbing phe-

nomenon in the changing OTDR traces. Environmental noises

such as wind, rain and other changing climates, unpredictable

and unavoidable inﬂuence of trafﬁcs, mechanical vibrations and

shocks induced by construction work, could all obscure real

human intrusions and cause high nuisance alarm rates (NARs).

Even though some methods are proposed for detection perfor-

mance improvement, such as using heterodyne detection [11],

wavelet denoising [12] and two-dimensional edge detection

[13], they are all focused on the improvement of the disturbance

detection sensitivity but not extraction or determination of the

disturbing signals; also, they still cannot tell if it is caused by

the slow change of the system and the environmental interfer-

ences. While this problem is more important and more challeng-

ing in practical applications of the above mentioned areas. In

Ref. [14], some ﬂuctuant background noises are suppressed by

the background energy subtraction along the spatial length. And

Ref. [15] presents some results of disturbing signal separation

from the slowly ﬂuctuant background but details of the adopted

method are not mentioned and the authors have no regard for

the time-varying environmental inﬂuences.

In this paper, a novel and effective signal separation method is

proposed to extract real human intrusions from the complicated

noisy background and determine them. Instead of using the

conventional differential OTDR curve, time-evolving signals

for each spatial point are accumulated and analyzed; multi-scale

wavelet decomposition and reconstruction is then employed to

separate real intrusions from the nonlinearly mixed signals. The

best increase of SNR in the spatial detecting signal can be as high

as ∼35dB. Moreover, different types of signals can be exposed

with different distribution features along with the decomposed

scales and thus they can be further determined. In this paper, by

physically separating real intrusions and other systematic and

environmental inﬂuences by the wavelet tool, we also construct a

3-layer back propagation (BP) artiﬁcial neural network (ANN)

network to identify the different distributions of three typical

events, no intrusion, human intrusion, and hand clapping for

simulating the ambient time-varying sounds and air movements

in the environments.

See http://www.ieee.org/publications

standards/publications/rights/index.html for more information.

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