Real-time Distributed Fiber Optic Sensor for Security Systems: Performance, Event Classification and Nuisance Mitigation

Abstract

The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the nuisance alarm rate (NAR), and the false alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.

Keywords

Adaptive level crossings / fiber optic sensor / intrusion detection / nuisance alarm

References

[1] Kersey A. D.. A review of recent developments in fiber optic sensor technology. Optical Fiber Technology, 1996, 36(2): 291-317.

[2] Katsifolis J., McIntosh L.. Apparatus and method for using a counter-propagating signal method for locating events. U.S. Patent, 2009, 7, 177.

[3] Juarez J. C., Maier E. W., Choi K. N., Taylor H. F.. Distributed fiber-optic intrusion sensor system. Journal of Lightwave Technology, 2005, 23(6): 2081-2087.

[4] Juarez J. C., Taylor H. F.. Field test of a distributed fiber-optic intrusion sensor system for long perimeters. Applied Optics, 2007, 46(11): 1968-1971.

[5] S. Tarr and G. Leach, “The dependence of detection system performance on fence construction and detector location,” in Proceedings of the 32nd Annual IEEE International Carnahan Conference on Security Technology, pp. 196–200, 1998.

[6] L. H. Jiang, X. M. Liu, and F. Zhang, “Multi-target recognition used in airpoty fiber fence warning system,” in Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, Qingdao, Jul. 11–14, pp. 1126–1129, 2010.

[7] J. D. Vries, “A low cost fence impact classification system with neural networks,” in Proceedings of 7th AFRICON Conference in Africa, Sept. 15–17, vol. 1, pp. 131–136, 2004.

[8] A. Yousefi, A. A. Dibazar, and T. Berger, “Intelligent fence intrusion detection system: detection of intentional fence breaching and recognition of fence climbing,” in IEEE International Conference on Technologies for Homeland Security, Boston, May 12–13, pp. 620–625, 2008.

[9] H. Min, C. Lee, J. Lee, and C. H. Park, “Abnormal signal detection in gas pipes using neural networks,” in Proceeding of 33rd Annual Conference of the IEEE Industrial Electronics, Taiwan, Nov. 5–8, pp. 2503–2508, 2007.

[10] Mahmoud S., Katsifolis J.. Robust event classification for a fiber optic perimeter intrusion detection system using level crossing features and artificial neural networks. Proc. SPIE, 2010, 7677, 767708.

[11] Rabiner L. R., Schafer R. W.. Digital Processing of Speech Signals, 1978, London: Pearson Education

[12] Junqua J. C., Mak B., Reaves B.. A robust algorithm for word boundary detection in the presence of noise. IEEE Transactions on Speech and Audio Processing, 1994, 2(3): 407-412.

[13] Mahmoud S., Katsifolis J.. Elimination of rain-induced nuisance alarms in distributed fiber-optic perimeter intrusion detection systems. Proc. SPIE, 2009, 7316, 731604-1-731604-11.

[14] Freeman A., Skapura M.. Neural Networkss: Algorithms, Applications, and Programming Techniques, 1991, Massachusetts: Addison-Wesley

[15] Jain A. K., Mao J., Mohiuddin K. M.. Artificial neural networks: a tutorial. Computer, 1996, 29(3): 31-44.