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An Improved Positioning Algorithm With High Precision for Dual Mach–Zehnder Interferometry Disturbance Sensing System Qinnan Chen, Tiegen Liu, Kun Liu, Junfeng Jiang, Zhe Shen, Zhenyang Ding, Haofeng Hu, Xiangdong Huang, Liang Pan, and Chunyu Ma
Abstract—An improved positioning algorithm for dual Mach– Zehnder interferometry (DMZI) disturbance sensing system is proposed. We employ zero-crossing method, which can be computed easily to extract the disturbance signal segment with maximum zero-crossing rate. Meanwhile, we use general cross correlation based on Wiener filtering and Gn n subtraction weighting function (WG-GCC) to estimate the time delay of the extracted signal, which is robust to the correlated noise. Finally, we experimentally demonstrate that the proposed positioning algorithm can greatly improve the positioning accuracy with positioning error of ±20 m. Compared with the traditional positioning algorithm, the positioning error has been reduced by an order of magnitude. This algorithm has a promising potential in real-time fence perimeter applications. Index Terms—Distributed fiber-optic sensor, fence perimeter application, general cross correlation, positioning algorithm, zerocrossing rate.
I. INTRODUCTION UAL Mach–Zehnder interferometry (DMZI) disturbance sensing system is widely used in perimeter security monitoring, pipeline leakage detection, submarine cable security monitoring, and other applications [1]–[8] due to advantages of high sensitivity, fast response, and simple structure [4]–[8]. It can obtain disturbance position by applying time delay estimation algorithm, which lies at the heart of positioning algorithm. Currently, the most popular time delay estimation algorithm is to do cross correlation between the two output signals [1]–[7]. It will bring a huge amount of computation thought it is easily achieved, which influences real-time performance of the sensing system and its positioning error is easily induced by diverse noises of output signals [9]. In general, the noise sources which
D
Manuscript received August 17, 2014; revised November 28, 2014; accepted January 18, 2015. Date of publication February 15, 2015; date of current version March 16, 2015. This work was supported in part by the National Basic Research Program of China under Grant 2010CB327806, in part by National Instrument Program under Grant 2013YQ030915, in part by the National Natural Science Foundation of China under Grants 61475114, 61108070, 11004150, 61227011 and 61378043, and in part by the Tianjin Science and Technology Support Key Project under Grant 11ZCKFGX01900. (Corresponding author: K. Liu.) Q. Chen, T. Liu, K. Liu, J. Jiang, Z. Ding, H. Hu, L. Pan, and C. Ma are with the College of Precision Instrument & Opto-electronics Engineering, Tianjin University, Tianjin 300072, China (e-mail:
[email protected]). Z. Shen is with the Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, U.K. (e-mail:
[email protected]). X. Huang is with the School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China (e-mail:
[email protected]). 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.2015.2394494
affect the positioning error include the frequency noise from the laser source, the polarization noise and the environment noise. The frequency noise and the polarization noise are ubiquitous but can be reduced to some extent by compensating the length difference between interferometer arms and dynamically adjusting the polarization state of the light in the interferometer, respectively [4], [10]. The environment noise is negligible in some cases such as submarine cable security monitoring, but it is the major noise source in some applications such as fence perimeter. Therefore, the main source of noise is not the same in different applications. As the aforementioned noises are difficult to eliminate, researchers pay more attention to the positioning algorithms for reducing noise. Xie et al. analyzed the positioning error of DMZI sensor and proposed a positioning error reduction technique. They used a high-pass filter to reshape the original power spectrum, and achieve a lower mean square error of the crosscorrelation based positioning algorithm [4], [6]. It is suitable for submarine cable security application rather than fence perimeter application as the environment noise was neglected. Wu et al. employed endpoint detection technologies such as discrete wavelet to extract the effective signal segment at starting point of disturbance before applying cross correlation [9], [11], [12]. However, there is no obvious starting point when intrusion occurs in fence perimeter application and the positioning error of cross correlation based algorithm is easily affected by the environment noise induced by slight vibration along the sensing cable [10]. Although they obtained a certain degree of accuracy, there is not enough in practical application. We theoretically analyze the positioning error of the DMZI sensing system by taking into account the environment noise. Based on the theory, we proposed an improved positioning algorithm with high precision for more general applications. As far as we know, it is the first time to focus on the positioning algorithm especially for fence perimeter application. Compared with the traditional positioning algorithm, our method has some improvements. First, we extract the signal segment with highest zero-crossing rate, which has higher positioning accuracy instead of endpoint extraction to estimate time delay. Moreover, the signal extraction is based on zero-crossing technique, which has advantages of easy implementation and high efficiency [13], [14]. Furthermore, in order to remove the correlated noise, general cross correlation based on Wiener filtering and Gnn subtraction (WG-GCC) weighting function is used to estimate the time delay [12], [15], [16]. We experimentally demonstrated
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CHEN et al.: AN IMPROVED POSITIONING ALGORITHM WITH HIGH PRECISION FOR DUAL MACH–ZEHNDER INTERFEROMETRY
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where s(t), n1 (t), and n2 (t) are real, jointly stationary random process. Assuming that signal s(t) is uncorrelated with noise n1 (t) and n2 (t). We can easily estimate the time delay d by locating the peak position of the cross correlation function between the two signals in (3), and then, calculate the vibration position from (1). However, in the actual situation, considering the related noises, the general noise-involved model of interference signal should be expressed as [4]
Fig. 1. Schematic diagram of DMZI disturbance sensing system. DAQ: Data Acquisition Card; IPC: Industrial Personal Computer; C1, C4, C5: 3 dB fiber coupler; C2, C3: Optical circulator; PD1, PD2: Photo-detector.
that the positioning algorithm can improve the positioning accuracy of the sensor in fence perimeter application. II. THEORY A DMZI disturbance sensing system is shown in Fig. 1. The output of the laser with narrow line-width is split equally at coupler C1. The two light beams are launched into a dual Mach– Zehnder interferometer formed by coupler C4 and coupler C5 after passing through circulator C2 and circulator C3, respectively. The two light beams propagate oppositely in clockwise and counter-clockwise directions and interfere at peer coupler. The interference outputs will be detected by PIN diodes PD1 and PD2, respectively. The output signals of the PIN diodes are acquired by data acquisition card (DAQ) and processed in industrial personal computer (IPC). When a disturbance event occurs at point P which has a distance x from the point A on the fiber, there will be an arrival time delay d between the two channel signals detected by PD1 and PD2. The time delay d can be expressed as d = n(L − 2x)/c
(1)
It indicates that the disturbance position can be deduced from the time delay d, as the velocity of light in vacuum c, the effective refractive index of fiber n and the length of interferometer arm L are all constants for the system. For an ideal system, the ac components of the output signals detected by PD1 and PD2 are I1 (t) = cos(φ(t)) + nc1 (t) (2) I2 (t) = cos(φ(t − d)) + nc2 (t) where φ(t) is the phase modulation difference between the two arms of the interferometer caused by the disturbance event without phase noise induced by polarization, while nc1 and nc2 are the additive circuit noise. The most popular time delay estimation technique is to perform cross correlation between two channels, which is based on the mathematical model [17] I1 (t) = s(t) + n1 (t) (3) I2 (t) = s(t − d) + n2 (t)
I(t) = [1 + na (t)] · cos[φ(t) + ξ(t) + nε (t) + np (t)] + nc (t) (4) where ξ(t) is the additional environment noise introduced by the slight disturbance along the sensing fiber, nc is the additive circuit noise, na (t) and nε (t) are the visibility and phase noise induced by polarization effect, and np (t) is the phase noise coming from the frequency noise of the laser source. We can neglect the effect of the phase noise coming from the frequency noise of the laser source, as well as the visibility variation and phase noise induced by polarization effect. Because the former can be reduced by compensating the length difference between interferometer arms, and the latter, can be compensated by dynamically adjusting polarization state of the light in the interferometer [4], [10]. Then, the two output signals are simplified as I1 (t) = cos[φ(t) + ξ(t)] + nc1 (t) (5) I2 (t) = cos[φ(t − d) + ξ(t)] + nc2 (t) Direct use of model (3) instead of (5) will lead to information loss of non-additive noises and the wrong estimated result of d with cross correlations. In order to overcome this issue, we do a restriction and make two assumptions as follows. Restriction: The observation time T is set to a small value to satisfy that the environment noise is almost constant during the observation time. Assumption a: All processes in (5) are stationary random processes during the short observation time [4]. Assumption b: The cable vibration induced by intrusion is a simple harmonic oscillation in the short observation time. The environment noise can be expressed as ξ(t) = ξ(t0 ) + Δξ(t), t ∈ [0, T ], where ξ(t0 ) is a constant value and represents the environment noise at t0 . Under the aforementioned restriction, Δξ(t) ≈ 0 is a small variable changing with time. Equation (5) can be approximated as I1 (t) = cos[ϕ(t)] + Δξ(t) sin[ϕ(t)] + nc1 (6) I2 (t) = cos[ϕ(t − d)] + Δξ(t) sin[ϕ(t − d)] + nc2 which can be formed by a pure signal and an additive noise term as the model of (3), where ϕ(t) = φ(t) + ξ(t0 ), s(t) = cos[ϕ(t)], and n1 (t) = Δξ(t) sin[ϕ(t)] + nc1 (t), n2 (t) = Δξ(t) sin[ϕ(t − d)] + nc2 (t). In terms of the cross-correlation time delay estimation theory under Assumption a, we can estimate d by locating the peak position of the cross-correlation function between I1 (t) and I2 (t). When the received SNR (signal to noise ratio) is high, the minimum mean square error (MSE) of the positioning er-
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ror induced by delay value deviation from the correct peak of cross-correlation function follows Crammer–Rao lower bound (CRLB) model [15], [18]: 2 1 1 2 +1 (7) 1+ σC R L B = 2 SN R 8π 2 T B(f02 + B12 ) where B is signal bandwidth, f0 is center frequency, and T is the observation time. On the basis of Assumption b, the signal segment can be approximated as the baseband sinusoidal frequency modulation (SFM) signal with the form of u(t) = cos(βcos(2πf t)), where ϕ(t) = βcos(2πf t) is the phase change induced by vibration, β and f are amplitude and frequency, respectively. For the baseband SFM signal, the signal bandwidth is B ≈ 2(β + 1)f , and SNR, f are constants in an intrusion event, while f0 equals to 0 [19]–[21]. According to (7), the signal segment with larger bandwidth B has higher positioning precision as SNR, f0 and T are constant. So we should extract the large bandwidth signal segment for time delay estimation to obtain small MSE of positioning error. Meanwhile, the additional environment noise ξ(t) is induced by the weak disturbance along the sensing cable, and it has similar character with the disturbance event, which makes the noises of the two signals (n1 (t) = Δξ(t) sin[ϕ(t)] + nc1 (t) and n2 (t) = Δξ(t) sin[ϕ(t − d)] + nc2 (t)) strongly correlated. The cross correlation of I1 (t) and I2 (t) is RI 1 I 2 (τ ) = Rss (τ − d) + Rn 1 n 2 (τ ) ∞ Gss (f )e−j 2π f τ df = δ(τ − d) ∗
∞ −∞
Gn 1 n 2 (f )e−j 2π f τ df
Flow chart of the improved positioning algorithm.
Step 3: Use general cross correlation based on Wiener filtering and Gnn subtraction weighting function (WG-GCC) to estimate the time delay of the extracted signal, which is robust to the correlated noise. The flow chart and diagram of the positioning algorithm is shown as in Fig. 2. A. Signal Extraction Based on ZCR
−∞
+
Fig. 2.
(8)
where Gss (f ) is the auto power spectral density of s(t), Gn 1n 2 (f ) is the cross power spectral density of n1 (t) and n2 (t). Rn 1n 2 (τ ) is not equal to 0. It will cause the peak position of RI I I 2 (τ ) deviate from the time delay d. To solve this problem, we should use a general cross correlation, which is robust to the correlated noise to suppress the noise power. Therefore, in order to achieve high positioning accuracy, we should first set an extraction time according to the environment noise, and extract the largest bandwidth signal segment, then estimate the time delay of the extracted signal segments by using a general cross correlation. III. POSITIONING ALGORITHM The proposed positioning algorithm consists of the following steps: Step 1: Set extraction time. Through the long-term analysis, we find the environment phase noise is almost constant during the observation time of 10 millisecond order of magnitude. In order to meet the restriction in Section II and obtain sufficient data for time delay estimation, we set the extraction time as 0.02 s by compromise. Step 2: Exact the largest bandwidth signal segment based on zero-crossing rate.
In acoustic signal processing applications such as speech recognition, zero-crossing analysis is commonly used to distinguish the sounds of different frequencies [14]. In the context of discrete-time signals, zero crossing will occur if successive samples have different algebraic signs. Being a simple method for signal frequency analysis, zero-crossing rate is a measurement of number of times in a given frame that the amplitude of the signals passes through zero. A definition of zero-crossings rate is ZCRn =
∞
|sgn[x(m)] − sgn[x(m − 1)]|ω(n − m)
−∞
sgn[x(n)] =
=
1
x(n) ≥ 0
−1 x(n) < 0 1/2N 0
and ω(n)
0≤n≤N −1 otherwise
(9)
where N is the length of a selected frame. The distribution of the zero-crossing rates can be obtained by applying (9) to the signal. Zero-crossing rate is measurement of “frequency composition” of a signal. This is more valid for narrowband signals such as sinusoids. The interference signal is a broadband signal and the interpretation of zero-crossing rate is, therefore, much less precise, but we can roughly estimate the spectral properties according to the short time average zero-crossing rate value [13], [14]. Since larger bandwidth indicates more high-
CHEN et al.: AN IMPROVED POSITIONING ALGORITHM WITH HIGH PRECISION FOR DUAL MACH–ZEHNDER INTERFEROMETRY
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frequency components, there is a strong correlation between zero-crossing rate and bandwidth. We can easily find the peak position of the zero-crossing rate curve, which corresponds to largest bandwidth. Then, we can extract the signal segment around the peak position. B. Time Delay Estimation Based On WG-GCC
Fig. 3.
A typical sensing cable configuration on chain link fence.
After extracting the largest bandwidth signal segments, we can estimate the time delay between the selected signals. If we assumed that the correlated noises are stationary or short-time stationary, we can estimate Gn 1n 2 (ω) and |Ni (ω)|2 through undisturbed signal [12]. According to (8), estimation error, Fourier transformed of Rn 1n 2 (τ ), is mainly determined by Gn1n2 (ω). So we can subtract the noise spectrum to reduce the estimation error. Based on the aforementioned analysis, we use WG-GCC, which is robust in the correlated and reverberate noise environment to estimate the time delay [16]–[18]. The general cross correlation method based on Wiener filtering and Gnn subtraction can be expressed as follows: τ = arg max Rs 1 s 2 (τ ) π 1 Gs s (ω)ej ω τ dω Rs 1 s 2 (τ ) = 2π −π 1 2 π 1 W1 (ω)W2 (ω)(GI 1 I 2 (ω) ≈ 2π −π
Fig. 4. Two second output signals in different events. (a) No intrusion. (b) Fence climb. (c) Fence cut.
− Gn 1 n 2 (ω))ej ω τ dω Wi (ω) = (|Ii (ω)|2 − |Ni (ω)|2 )/ |Ii (ω)|2 , i = 1, 2 (10) where Rs1s2 (τ ) is the cross correlation between s1 (t) and s2 (t) in (6), which is equivalent to the inverse Fourier transform of Gs1s2 (ω). Wi (ω) is the Wiener filtering weighting function. Then, we can estimate the time delay d by locating the peak position of Rs1s2 (τ ). IV. EXPERIMENTS In this section, we first analyze the signals in different intrusion events to demonstrate the rationality of the assumptions in the positioning theory. Then, we conduct a positioning experiment based on zero-crossing technique and WG-GCC to verify the validation of the proposed algorithm. Finally, 500 sets of positioning tests by using three different algorithms are conducted to illustrate the performance of the algorithm. The experiment setup is shown in Fig. 1. Laser source was a 1550 nm distributed feedback laser. The output light with 3.5 mW is split equally at C1, and then, the two light are launched into the dual Mach–Zehnder interferometer formed by C4 and C5. The two light beams propagate oppositely in clockwise and counter-clockwise directions along a 2.25 km (L = 2.25 km) single mode sensing cable, which is attached to perimeter fence, and interfere at the peer coupler. The interference outputs are detected by PD1 and PD2, respectively, collected by DAQ and processed by IPC. The data sampling rate of DAQ is set to 10 MHz and the theoretical resolution is
10 m. In the experiments, the intrusion is generated by people of 60 kg weight climbing and cutting the fence near the post and the distance between the vibration point P and point A is 620 m. The implementation of the sensor as a fence perimeter system is achieved by attaching the sensing cable directly to the fence. The recommended fence fabrics include chain link, weld mesh, and palisade styles, which vibrate strongly when intrusion occurs. In order to maintain the long-term stability of the state of polarization and avoid excessive nuisance signals, the fence construction needs to follow an acceptable standard and the sensing cable should be directly attached to the fence fabric through hose clamps. Fig. 3 shows a sensing cable configuration on chain link fence. We loop the cable up and down along the posts to improve the detection sensitivity of fence when people climb at or near the rigid posts. It should be noted that the cable configuration depends on the required level of security, the type of intrusion, and the skill level of intruder. Whatever the configuration is, it will induce a significant environment noise ξ(t), which cannot be neglected since it blows and rains in the outdoor environments. A. Demonstration of the Theory Fig. 4 shows the signals in three different events including no intrusion, fence climb and fence cut. As can be seen from the figure, the environment noise changes much more slowly than the phase change induced by the intrusion event. It means that the restriction of the theory is reasonable. The output signal induced by intrusion action shows great irregularity during the intrusion,
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TABLE I POSITIONING ERRORS OF DIFFERENT SIGNAL REGIONS Region Maximum ZCR of region Positioning error/m (CC) Positioning error/m (WG-GCC)
1
2
3
4
5
0.117 20 10
0.105 30 20
0.078 80 80
0.050 90 70
0.039 120 120
TABLE II POSITIONING ERROR STATISTICS OF DIFFERENT ALGORITHMS TDE algorithm WG-GCC with extraction CC with extraction CC without extraction
Fig. 5.
Mean absolute error
Standard deviation
4.4000 11.2000 50.8000
7.0345 13.8137 59.3993
Short-time Fourier transform of the fence climb signal in Fig. 4 (b).
four regions with low zero-crossing rate as shown in Fig. 6 and apply crossing correlation and WG-GCC to the five signal regions, where the zero-crossing rate is sorted in descending order. Table I shows the positioning errors of the five regions. It is obvious that positioning error based on cross correlation is smaller in high zero-crossing rate signal region than relatively low zero-crossing rate region and WG-GCC is effective in improve positioning accuracy. C. Performance of the Algorithm Fig. 6. A Two second period of fence climb signal. (a) Time domain representation. (b) Zero crossing rate representation.
intuitively showing that there are different signal densities at different time periods. It is difficult to determine the start point of the event, so the positioning algorithm based on endpoint detection is unpractical in this situation. Fig. 5 shows the short-time Fourier transform of the fence climb signal in Fig. 4(b). We can see that the disturbance signal is a non-stationary signal and there are irregular changes in spectrum with time. The dense regions of the signal have more high frequency components, which is corresponding to larger bandwidth, while the sparse regions have less high frequency components, which is corresponding to smaller bandwidth. It agrees with the corollary under Assumption b and supports the theory in Section II. B. Validation of the Algorithm We conduct a positioning experiment to a fence climb signal shown in Fig. 6 to verify the validation of the proposed algorithm. Fig. 6(b) shows the distribution of the zero crossing rate of the signal. According to the positioning theory, the signal segment with higher zero crossing rate, which is corresponding to larger bandwidth has higher positioning accuracy. So we extract the signal region 1 as shown in Fig. 6, which is around the peak position of the distribution of the zero crossing rates for time delay estimation. 200 k samples are selected according to the extraction time (0.02 s). We also extract the other
In the real application, the signal is divided in to continuous signal frames. Considering the real-time performance, we set the frame length to be 0.3 s. Three different algorithms are applied to 500 sets of positioning tests. The first two algorithms are estimating the time delay of the extracted signals by WGGCC and traditional cross correlation, respectively, while the third algorithm is estimating the whole frame by traditional cross correlation. We mark the three different algorithms as WG-GCC with extraction, CC with exaction and CC without extraction respectively for short. Table II shows the statistics of the positioning error of different algorithms. We can see from Table II that WG-GCC with extraction can effectively reduce the mean absolute error and standard deviation of the positioning error compared with CC with and without extraction. In particular, the positioning error of the proposed algorithm has been reduced by an order of magnitude compared to CC without extraction. Further analysis of the positioning result reveals that the positioning error can be basically reduced to the range of 0 ∼±20 m by applying WG-GCC with signal extraction based on zerocrossing rate, of which the mean absolute error and standard deviation are 4.4000 m and 7.0345 m, respectively. In particular, there is 60.16% of the positioning error distributed in the range of 0 ∼±10 m, which is close to the theoretical precision, and up to 83.66% distributed in the range of 0 ∼±20 m, only a minor part of the results represent that the positioning errors are greater than ±20 m. Fig. 7 shows the running time of 500 sets of positioning tests conducted by three different algorithms, we can see that
CHEN et al.: AN IMPROVED POSITIONING ALGORITHM WITH HIGH PRECISION FOR DUAL MACH–ZEHNDER INTERFEROMETRY
Fig. 7.
Running time of three different algorithms.
the running times of CC with extraction and WG-GCC with extraction are much less than 0.3 s, which fully satisfy the real-time performance, while the running times of CC without extraction are all distributed near 1s which is far beyond the frame length, it severely affect the efficiency and result in data loss. V. CONCLUSION We theoretically analyze the positioning error of the DMZI sensing system by taking into account the environment noise. Following this theoretical basis, an improved positioning algorithm with high precision and easy implementation is employed. We first extract the signal segment with highest zero-crossing rate, then use general cross correlation based on Wiener filtering and Gnn subtraction (WG-GCC) weighting function, to estimate the time delay of the extracted signals. Although the proposed algorithm has advantages of high precision, easy implementation, and high efficiency, its performance is limited fundamentally by the sampling interval, next step we will move on to the study of estimating continuous time delay from sampled data and we have already got some preliminary results. Finally, we have experimentally demonstrated that the proposed positioning algorithm can greatly improve the positioning accuracy, with the positioning error of ±20 m. The proposed algorithm has a promising potential in real-time fence perimeter applications. REFERENCES [1] B. Kizlik, “Fibre optic distributed sensor in Mach-Zehnder interferometer configuration,” in Proc. Int. Conf. Modern Problems Radio Eng., Telecommun. Comput. Sci., 2002, pp. 128–130. [2] S. Liang et al., “Fiber-optic intrinsic distributed acoustic emission sensor for large structure health monitoring,” Opt. Lett., vol. 34, pp. 1858–1860, Jun. 15 2009. [3] Y. Liu, L. Wang, C. Tian, M. Zhang, and Y. Liao, “Analysis and optimization of the PGC method in all digital demodulation systems,” J. Lightw. Technol., vol. 26, no. 18, pp. 3225–3233, Sep./Oct. 2008. [4] S. R. Xie, Q. Zou, L. Wang, M. Zhang, Y. Li, and Y. Liao., “Positioning error prediction theory for dual Mach–Zehnder interferometric vibration sensor,” J. Lightw. Technol., vol. 29, no. 3, pp. 362–368, Feb. 1, 2011. [5] Y. Zhou et al., “Study on the distributed optical fiber sensing technology for pipeline leakage protection,” Proc. SPIE, vol. 6344, pp. 634435-1–634435-6, 2006.
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[6] S. Xie et al., “Positioning method for dual Mach-Zehnder interferometric submarine cable security system,” Proc. SPIE, vol. 7677, pp. 76770A-1–76770A-4, 2010. [7] Q. Sun et al., “Distributed disturbance sensor based on a novel MachZehnder interferometer with a fiber-loop,” Proc. SPIE, vol. 6344, pp. 63440K-1–63440K-(7, 2006. [8] Q. Sun, et al., “Distributed fiber-optic sensor with a ring MachZehnder interferometer,” Proc. SPIE, vol. 6781, pp. 67814D-1–67814D-8, 2007. [9] X. Shang-ran, et al., “A study on real-time location method for long distance dual MZ interferometric vibration sensor,” J. Optoelectron. Laser, vol. 20, no. 8, pp.1020–1024, 2009. [10] Q. N. Chen, T. Liu, K. Liu, J. Jiang, Z. Ding, L. Zhang, Y. Li, L. Pan, and C. Ma, “An elimination method of polarization-induced phase shift and fading in dual Mach-Zehnder interferometry disturbance sensing system,” J. Lightw. Technol., vol. 31, no. 19, pp. 3135–3141, Oct. 1, 2013. [11] W. Hongyan, et al., “Study on endpoint detection technology based on fiber perimeter security system,” Chin. J. Sci. Instrum., vol. 34, no. 4, pp. 743–748, 2013. [12] Y. Zhang et al., “Robust time delay estimation of distributed optical fiber sensor system,” J. Appl. Opt., vol. 33, pp. 815–820, 2012. [13] R. Bachu et al., “Separation of voiced and unvoiced using zero crossing rate and energy of the speech signal,” in Proc. Amer. Soc. Eng. Education Zone Conf., 2008, pp. 1–7. [14] S. S. Mahmoud and J. Katsifolis, “Elimination of rain-induced nuisance alarms in distributed fiber optic perimeter intrusion detection systems,” in Proc. SPIE Defense, Security, and Sensing, 2009, pp. 731604-1–731604-11. [15] I. Cespedes, J. Ophir, and S.K. Alam, “The combined effect of signal decorrelation and random noise on the variance of time delay estimation,” IEEE Trans. Ultrason., Ferroelectr. Freq. Control, vol. 44, no. 1, pp. 220–225, Jan. 1997. [16] Y. Rui and D. Florencio, “Time delay estimation in the presence of correlated noise and reverberation,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., 2004, vol. 2, pp. ii-133–6. [17] C. Knapp and G. C. Carter, “The generalized correlation method for estimation of time delay,” IEEE Trans. Acoust., Speech Signal Process., vol. 24, no. 4, pp. 320–327, Aug. 1976. [18] W. F. Walker and G. E. Trahey, “A fundamental limit on delay estimation using partially correlated speckle signals,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol. 42, no. 2, pp. 301–308, Mar. 1995. [19] R. Ziemer and W. H. Tranter, Principles Of Communications: System Modulation and Noise. Hoboken, NJ, USA: Wiley, 2006. [20] D. J. Inman and R. C. Singh, Engineering Vibration, vol. 3. Englewood Cliffs, NJ, USA: Prentice-Hall, 2001. [21] E. Udd, Fiber Optic Sensors. New York, NY, USA: Wiley Online Library, 1993. Qinnan Chen was born in Fujian, China, in 1987. He received the B.Sc. degrees in opto-electronic technology science (Cooperate with Nankai University) from Tianjin University, Tianjin, China, in 2010, where he is currently working toward the Ph.D. degree of optical engineering. His research interests mainly focus on distributed fiber sensing.
Tiegen Liu, biography not available at the time of publication.
Kun Liu, biography not available at the time of publication.
Junfeng Jiang, biography not available at the time of publication.
Zhe Shen, biography not available at the time of publication.
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Zhenyang Ding, biography not available at the time of publication.
JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 33, NO. 10, MAY 15, 2015
Liang Pan, biography not available at the time of publication.
Haofeng Hu, biography not available at the time of publication.
Xiangdong Huang, biography not available at the time of publication.
Chunyu Ma, biography not available at the time of publication.
