﻿ 基于分布式传感光纤的模式识别及定位技术
 计算机系统应用  2019, Vol. 28 Issue (6): 95-99 PDF

Pattern Recognition and Position Technology Based on Distributed Sensing Fiber
HE Long-Zhou, SUN Jie
College of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Abstract: With the rapid development of the economy, social security has become quite important. At the same time, the criminal methods used by criminals are more complicated and high-tech. Traditional security measures are mostly based on electrical sensing or relying on human surveillance, and are hard to guarantee people’s property and personal safety effectively. Therefore, this study proposes a fiber perimeter system based on double M-Z structure, which uses the characteristics of the fiber to identify the pattern of interference signal, and then uses the special structure of entire perimeter system to respond to the interference signal with high efficiency and alarm, and even complete accurate positioning function. Through the signal processing technology, the signal-to-noise ratio of the acquired signal is increased, and a good recognition rate and a high positioning accuracy are achieved.
Key words: fiber perimeter     pattern recognition     double M-Z     positioning

1 双M-Z分布式光纤传感周界系统

 图 1 M-Z结构的光纤周界系统原理图

2 周界系统的调制原理

 $\varphi {\rm{ = }}\frac{{{\rm{2}}\pi n}}{{{\lambda _{\rm{0}}}}}L = \frac{{2\pi }}{\lambda }L = \beta L$ (1)

 $\Delta \varphi = \beta \Delta L + L\Delta \beta = \beta L\frac{{\Delta L}}{L} + L\frac{{\partial \beta }}{{\partial n}}\Delta n + L\frac{{\Delta \beta }}{{\Delta r}}\Delta r$ (2)

 $\Delta \varphi = \beta \Delta L + L\Delta \beta = \beta L\frac{{\Delta L}}{L} + L\frac{{\partial \beta }}{{\partial n}}\Delta n$ (3)
3 系统的定位原理

X=(t1c)/n; 则干扰信号距离耦合器C4的距离为:L1X=(t2c)/n; 耦合器C3、C4之间的距离为: L2=(t3c)/n; 上述各式中c为真空中的光速, n为光纤纤芯的折射率.

 ${y_1}(t) = {I_1}[1 + {Q_1}\cos (\varphi (t - {t_1}) + {\varphi _0}]$ (4)

 ${y_2}(t) = {I_2}[1 + {Q_2}\cos (\varphi (t - {t_2} - {t_3}) + {\varphi _0}]$ (5)

 $T = {t_2} + {t_3} - {t_1} = \frac{{({L_1} + {L_2} - 2X)n}}{c}$ (6)

 $X = \frac{{(L1 + L2 - cT/n)}}{2}$ (7)

4 实验的结果及分析 4.1 正交调制的选择

 图 2 正交偏置仿真

4.2 采集信号的模式识别

 图 3 模式识别(正常状态)

 图 4 模式识别(有风状态)

 图 5 模式识别(人为入侵)

4.3 周界系统的定位研究

 图 6 干扰信号定位程序

 图 7 双路信号互相关程序

5 结论

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