An optic-fiber fence intrusion recognition system using the optimized curve fitting model based on the SVM method

• Published in: 2018 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 6
• Main Authors: He, Ningyu, Zhu, Jie, Li, Lianqiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2018
• Subjects: Back Propagation Neural Networks (BPNN); Feature extraction; Intrusion detection; Optic-fiber; Optical computing; Optical interferometry; Optical scattering; Optical sensors; Perimeter intrusion; Recognition; Sagnac interferometer; Support vector machines; Support Vector Machines (SVM); The optimized curve fitting model
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2018.8489681

The Perimeter Intrusion Detection System (PIDS) has been widely used in many fields since the development of optic-fiber interferometers and intrusion signal recognition models. However, common signal recognition models, such as Support Vector Machines (SVM) and Back Propagation Neural Networks (BPNN), do not perform well in classifying fiber intrusion signals due to the diversity of intrusion signals and the sensitivity of the fiber. In this paper, an optic-fiber based perimeter intrusion detection and recognition system that uses Sagnac interferometers and the optimized curve fitting model is proposed. Experiments on real perimeter intrusions are performed. Comparisons are carried out among our model and the SVM, BPNN models, which prove that our model is more accurate and robust.