A real-time intelligent fiber-optic perimeter intrusion detection system （PIDS） based on the fiber Bragg grating （FBG） sensor network is presented in this paper. To distinguish the effects of different intrusion e...A real-time intelligent fiber-optic perimeter intrusion detection system （PIDS） based on the fiber Bragg grating （FBG） sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal＇s profile in the time domain. The features are extracted by the principal component analysis （PCA）, which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate （IR） for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate （DR） and low false alarm rate （FAR） can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.展开更多
基金National High Technology Research and Development Program of China （863 Program, Grant No. 2007AA01Z245）, the supports provided for this research by the Major Program （Grant No. 61290312） and Youth Foundation （Grant No. 61301275） of the National Natural Science Foundation of China （NSFC）, and the Fundamental Research Funds for the Central Universities （Grant No. ZYGX2011J010）. This work is also supported by Program for Changjiang Scholars and Innovative Research Team in University （PCSIRT, IRT1218）, and the 111 Project （B14039）.
文摘A real-time intelligent fiber-optic perimeter intrusion detection system （PIDS） based on the fiber Bragg grating （FBG） sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal＇s profile in the time domain. The features are extracted by the principal component analysis （PCA）, which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate （IR） for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate （DR） and low false alarm rate （FAR） can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.
