We propose a fully distributed fusion system combining phase-sensitive optical time-domain reflectometry(Φ-OTDR) and OTDR for synchronous vibration and loss measurement by setting an ingenious frequency sweep rate(FS...We propose a fully distributed fusion system combining phase-sensitive optical time-domain reflectometry(Φ-OTDR) and OTDR for synchronous vibration and loss measurement by setting an ingenious frequency sweep rate(FSR) of the optical source. The relationships between FSR, probe pulse width and repeat period are given to balance the amplitude fluctuation of OTDR traces, the dead zone probability and the measurable frequency range of vibration events. In the experiment, we achieve synchronous vibration and loss measurement with FSR of 40 MHz/s, the proble pulse width of 100 ns and repeat rate of 0.4 ms. The fluctuation of OTDR trace is less than 0.45 dB when the signalto-noise ratio(SNR) is over 12 dB for a captured vibration event located at 9.1 km. The proposed method can be used for not only detection but also early warning of damage events in optical communication networks.展开更多
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 ...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 100mm/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.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61405090,61307096 and 61540017)
文摘We propose a fully distributed fusion system combining phase-sensitive optical time-domain reflectometry(Φ-OTDR) and OTDR for synchronous vibration and loss measurement by setting an ingenious frequency sweep rate(FSR) of the optical source. The relationships between FSR, probe pulse width and repeat period are given to balance the amplitude fluctuation of OTDR traces, the dead zone probability and the measurable frequency range of vibration events. In the experiment, we achieve synchronous vibration and loss measurement with FSR of 40 MHz/s, the proble pulse width of 100 ns and repeat rate of 0.4 ms. The fluctuation of OTDR trace is less than 0.45 dB when the signalto-noise ratio(SNR) is over 12 dB for a captured vibration event located at 9.1 km. The proposed method can be used for not only detection but also early warning of damage events in optical communication networks.
文摘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 100mm/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.
