Research On Wireless Perimeter Intrusion Monitoring System And Recognition Of Its Intrusion Signals

Vibration sensor's signals are collected from perimeter intrusion monitoring system, which include a variety of interference signals caused by environment. It is important and practical to separate the source of vibration intrusion signals from those signals. Using blind source separation (BSS) technique to recognize the source of vibration intrusion signals is an important and challenging research field. With careful study and comparison with several typical blind source separation methods, recognition methods are introduced to adapt to wireless perimeter intrusion monitoring system and its vibration sensor'signals. The main contributions are as follows:With careful study and comparison with several typical blind source separation methods, four kinds of typical algorithms based on ICA are implemented in this thesis. Experimental data are vibration sensor'intrusion signals, which are collected from wireless perimeter intrusion monitoring system. Similarity coefficient and performance index are used to evaluate the performance of four kinds of algorithms. Comparing JADE algorithm with the Infomax algorithm, FastICA algorithm and SOBI algorithm respectively, which are all based on ICA algorithm. Experiments show that JADE algorithm is better than other three algorithms. Therefore, JADE algorithm is suitable for recognition of vibration sensors'intrusion signals, which are collected from wireless perimeter intrusion monitoring system.A blind source separation algorithm named OP-BSS is proposed, which is based on Overlap Information Entropy (OIE) and Particle Swarm Optimization (PSO). The OIE is an objective function in blind source separation algorithm, and PSO is used as optimization algorithm. Experimental data are synthetic data and vibration sensor'intrusion signals severally. The experimental results show that this method can effectively recognize the source of vibration sensors'intrusion signals. On the basis of above research, identification methods of vibration intrusion signals are applied to wireless perimeter intrusion monitoring system, which is developed independently. The system contains four modules, which are network nodes, routing nodes, central node and upper computer alarm monitoring software. The test results show that the system functions are correct, and the desired goal is achieved.