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

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.

[1]  Jan Sijbers,et al.  A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test , 2014, Signal Process..

[2]  Graham V. Weinberg.  Management of interference in Pareto CFAR processes using adaptive test cell analysis , 2014, Signal Process..

[3]  Zvi Drezner,et al.  Normalizing variables with too-frequent values using a Kolmogorov-Smirnov test: A practical approach , 2011, Comput. Ind. Eng..

[4]  Alireza Bahrampour,et al.  Resolution enhancement in long pulse OTDR for application in structural health monitoring , 2010 .

[5]  Hongxing Yang,et al.  Locating Gas Pipeline Leakage Based on Stimulus-response Method , 2014 .

[6]  Lijing Li,et al.  Localization mechanisms and location methods of the disturbance sensor based on phase-sensitive OTDR , 2014 .

[7]  Chunxi Zhang,et al.  Fiber-optic distributed sensor based on phase-sensitive OTDR and wavelet packet transform for multiple disturbances location , 2014 .

[8]  Xiaofeng Ma,et al.  Improved switching CFAR detector for non-homogeneous environments , 2013, Signal Process..

[9]  Hao Feng,et al.  A SVM-based pipeline leakage detection and pre-warning system , 2010 .

[10]  Wei Liang,et al.  Gas pipeline leakage detection based on acoustic technology , 2013 .

[11]  Wei Liang,et al.  Coupling relations and early-warning for “equipment chain” in long-distance pipeline , 2013 .

[12]  Viliam Makis,et al.  Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test , 2009 .

[13]  K. Jing,et al.  Time Prediction Model for Pipeline Leakage Based on Grey Relational Analysis , 2012 .

[14]  Ramon Nitzberg,et al.  Clutter Map CFAR Analysis , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Hermann Rohling,et al.  Radar CFAR Thresholding in Clutter and Multiple Target Situations , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Fan Zhu,et al.  Frequency division multiplexing OTDR with fast signal processing , 2012 .

[17]  Samuel H. Huang,et al.  A Kolmogorov-Smirnov statistic based segmentation approach to learning from imbalanced datasets: With application in property refinance prediction , 2012, Expert Syst. Appl..

[18]  S. D. Himonas,et al.  Automatic censored CFAR detection for nonhomogeneous environments , 1992 .

[19]  Stanislaw Osowski,et al.  Texture characterization based on the Kolmogorov-Smirnov distance , 2015, Expert Syst. Appl..

[20]  Massimo L. Filograno,et al.  Coherent Noise Reduction in High Visibility Phase-Sensitive Optical Time Domain Reflectometer for Distributed Sensing of Ultrasonic Waves , 2013, Journal of Lightwave Technology.

[21]  Xiaochuan Ma,et al.  Parametric Rao test for multichannel adaptive detection of range-spread target in partially homogeneous environments , 2015, Signal Process..