A harmful-intrusion detection method based on background reconstruction and two-dimensional K-S test in an optical fiber pre-warning system

Abstract

The key technology and main difficulty for optical fiber intrusion pre-warning systems (OFIPS) is the extraction of harmful-intrusion signals. After being processed by a phase-sensitive optical time-domain reflectometer (Φ-OTDR), vibration signals can be preliminarily extracted. Generally, these include noises and intrusions. Here, intrusions can be divided into harmful and harmless intrusions. With respect to the close study of signal characteristics, an effective extraction method of harmful intrusion is proposed in the paper. Firstly, in the part of the background reconstruction, all intrusion signals are first detected by a constant false alarm rate (CFAR). We then reconstruct the backgrounds by extracting two-part information of alarm points, time and amplitude. This ensures that the detection background consists of intrusion signals. Secondly, in the part of the two-dimensional Kolmogorov-Smirnov (K-S) test, in order to extract harmful ones from all extracted intrusions, we design a separation method. It is based on the signal characteristics of harmful intrusion, which are shorter time interval and higher amplitude. In the actual OFIPS, the detection method is used in some typical scenes, which includes a lot of harmless intrusions, for example construction sites and busy roads. Results show that we can effectively extract harmful intrusions.

Keywords

Optical fiber intrusion pre-warning / extraction of harmful-intrusion signals / two-dimensional K-S test

References

[1] Qu Z., Feng H., Zeng Z., Zhuge J., Jin S.. A SVM-based pipeline leakage detection and pre-warning system. Measurement, 2010, 43(4): 513-519.

[2] Kang J., Zou Z.. Time prediction model for pipeline leakage based on grey relational analysis. Physics Procedia, 1989, 25(2): 2019-2024.

[3] Liang W., Lu L., Zhang L.. Coupling relations and early-warning for ‘equipment chain’ in long-distance pipeling. Mechanical Systems and Signal Processing, 2013, 41(1–2): 335-347.

[4] Liang W., Zhang L., Xu Q., Yan C.. Gas pipeline leakage detection based on acoustic technology. Engineering Failure Analysis, 2013, 31(6): 1-7.

[5] Zhang T., Tan Y., Yang H., Zhao J., Zhang X.. Locating gas pipeline leakage based on stimulusresponse method. Energy Procedia, 2014, 61, 207-210.

[6] Lv Q., Li L., Wang H., Li Q., Zhong X.. Influences of laser on fiber-optical distributed disturbance sensor based on F-OTDR. Infrared and Laser Engineering, 2014, 43(12): 3918-3923.

[7] Martins H. F., Martin-Lopez S., Corredera P., Filograno M. L., Frazão O., Gonzáez M.. Coherent noise reduction in high visibility phase-sensitive optical time domain reflectometer for distributed sensing of ultrasonic waves. Journal of Lightwave Technology, 2013, 31(23): 3631-3637.

[8] Li Q., Zhang C., Li L., Zhong X.. Localization mechanisms and location methods of the disturbance sensor based on phase-sensitive OTDR. Optik–International Journal for Light and Electron Optics, 2014, 125(9): 2099-2103.

[9] Lin Q., Zhang C., Li C.. Fiber-optic distributed sensor based on phase-sensitive OTDR and wavelet packet transform for multiple disturbances location. Optik–International Journal for Light and Electron Optics, 2014, 125(24): 7235-7238.

[10] Qu Z., Feng H., Zeng Z., Zhuge J., Jin S.. A SVM-based pipeline leakage detection and pre-warning system. Measurement, 2010, 43(4): 513-519.

[11] Bahrampour A. R., Maaoumi F.. Resolution enhancement in long pulse OTDR for application in structural health monitoring. Optical Fiber Technology, 2010, 16(4): 240-249.

[12] Lu L., Song Y., Zhang X., Zhu F.. Frequency division multiplexing OTDR with fast signal processing. Optics & Laser Technology, 2012, 44(7): 2206-2209.

[13] Rohling H.. Radar CFAR thresholding in clutter and multiple target situations. IEEE Transactions on Aerospace and Electronic Systems, 1983, 19(4): 608-621.

[14] Himonas S. D., Barkat M.. Automatic censored CFAR detection for nonhomogeneous environments. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 286-304.

[15] Zhang R., Sheng W., Ma X.. Improved switching CFAR detector for non-homogeneous environments. Signal Processing, 2013, 93(1): 35-48.

[16] Weinberg G. V.. Management of interference in Pareto CFAR processes using adaptive test cell analysis. Signal Processing, 2014, 104(104): 264-273.

[17] Shi B., Hao C., Hou C., Ma X., Peng C.. Parametric Rao test for multichannel adaptive detection of range-spread target in partially homogeneous environments. Signal Processing, 2015, 108, 421-429.

[18] Nitzberg R.. Clutter map CFAR analysis. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(4): 419-421.

[19] Wang X., Makis V.. Auotoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test. Journal of Sound and Vibration, 2009, 327(3–5): 413-423.

[20] Swiderski B., Osowski S., Kruk M., Kurek J.. Testure characterization based on the Kolmogorov-Smirnov distance. Expert Systems With Applications, 2015, 42(1): 503-509.

[21] Rajan J., Dekker A. J. d, Sijbers J.. A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test. Signal Processing, 2014, 103(10): 16-23.

[22] Drezner Z., Turel O.. Normalizing variables with too-frequent values using a Kolmogorov-Smirnov test: a practical approach. Computer & Industrial Engineering, 2011, 61(4): 1240-1244.

[23] Gong R., Huang S.. A Kolmogorov-Smirnov statistic based segmentation approach to learning from imbalanced datasets: with application in property refinance prediction. Expert Systems with Applications, 2012, 39(6): 6192-6200.