For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system （OFPS） is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are interc...For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system （OFPS） is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform （MT） is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation （BP） neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.展开更多
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 61571014 and 61601006） Beijing Nature Science Foundation （Grant No. 4172017） General Project of Science and Technology Program of Beijing Education Commission （Grant No.KM201610009004）.
文摘For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system （OFPS） is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform （MT） is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation （BP） neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.
