本文選題：光纖預(yù)警系統(tǒng)　切入點：相關(guān)系數(shù)　出處：《北方工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：基于 Φ-OTDR(Phase-Sensitive Optical Time-Domain Reflectometer,相位敏感光時域反射計)的分布式光纖預(yù)警系統(tǒng)是一種新型的安防系統(tǒng)。其具有抗干擾性強、靈敏度高等優(yōu)點,可進行長距離、并發(fā)的入侵行為實時監(jiān)測。然而,光纖預(yù)警系統(tǒng)實際應(yīng)用中檢測到的入侵信號類型繁多、特性復(fù)雜,需對入侵信號進行有效的預(yù)處理及特征提取。本文圍繞這一技術(shù)難點,開展相應(yīng)的算法及工程實現(xiàn)研究,主要完成以下內(nèi)容:本文首先總結(jié)了光纖預(yù)警系統(tǒng)現(xiàn)狀及已有入侵信號特征提取方法,歸納出己有方法及其實現(xiàn)瓶頸,引出本文的研究思路及內(nèi)容。接著,介紹了本課題所構(gòu)建的光纖預(yù)警系統(tǒng)組成及工作原理。在光纖入侵信號預(yù)處理方面,根據(jù)光的雙折射現(xiàn)象對入侵信號進行時域相關(guān)性分析,發(fā)現(xiàn)時域上虛警與入侵信號的相關(guān)系數(shù)存在明顯差異。為此,本文提出一種基于時域相關(guān)系數(shù)的預(yù)處理方法。其中,為提升互相關(guān)系數(shù)計算效率,針對其中制約算法效率的小波去噪和相關(guān)計算兩個模塊,進行了實現(xiàn)層的優(yōu)化。實測數(shù)據(jù)驗證了本文預(yù)處理實現(xiàn)方法能有效去除虛警信號。在光纖入侵信號特征提取方面,本文研究了多種信號提取方法及其工程實現(xiàn)。在時域上提取占空比和基音周期特征,頻域上提取頻率中心特征,時頻域上提取小波系數(shù)能量占比特征;由多維特征構(gòu)建的特征向量對機械與人工信號具有很強的區(qū)分性。其中,為有效提取基音周期特征,本文提出了基于端點檢測預(yù)篩選的基音周期特征提取方法,有效提高了該特征提取的準確性。特征提取算法的工程實現(xiàn)是本文的重要研究內(nèi)容。針對光纖入侵信號特征提取的特點,本文基于DSP平臺設(shè)計特征提取實現(xiàn)架構(gòu),使用模塊化思想進行功能劃分,主要包括:DSP與上位機數(shù)據(jù)交互邏輯,DSP間的數(shù)據(jù)同步,DSP數(shù)據(jù)存儲。利用現(xiàn)場實際數(shù)據(jù)對本文提出的光纖入侵信號預(yù)處理及特征提取方法及其實現(xiàn),進行了驗證。
[Abstract]:The distributed optical fiber early warning system based on 桅 -OTDR Phase-Sensitive Optical Time-Domain reflection (Phase sensitive Optical time Domain reflectometer) is a new type of security system. The real-time monitoring of concurrent intrusion behavior. However, in the practical application of optical fiber early warning system, there are many types of intrusion signals detected and the characteristics are complex, so it is necessary to carry out effective preprocessing and feature extraction of intrusion signals. This paper focuses on this technical difficulty. The main contents are as follows: firstly, this paper summarizes the current situation of optical fiber early warning system and the existing intrusion signal feature extraction methods, and concludes the existing methods and their implementation bottlenecks. This paper introduces the research ideas and contents of this paper. Then, the composition and working principle of the optical fiber early warning system are introduced. According to the birefringence of light, the time-domain correlation analysis of the intrusion signal is carried out in the aspect of optical fiber intrusion signal preprocessing. It is found that the correlation coefficients of false alarm and intrusion signal are obviously different in time domain. Therefore, a preprocessing method based on time domain correlation coefficient is proposed in this paper. Aiming at the two modules of wavelet denoising and correlation calculation, which restrict the efficiency of the algorithm, the realization layer is optimized. The experimental data verify that the proposed preprocessing method can effectively remove false alarm signals. In this paper, a variety of signal extraction methods and their engineering implementation are studied. The characteristics of duty cycle and pitch period are extracted in time domain, frequency center feature is extracted in frequency domain, and wavelet coefficient energy ratio feature is extracted in time and frequency domain. The feature vectors constructed from multi-dimension features have strong distinctions between mechanical and artificial signals. In order to extract pitch cycle features effectively, a method of extracting pitch periodic features based on endpoint detection pre-screening is proposed in this paper. The engineering implementation of feature extraction algorithm is an important research content in this paper. According to the feature extraction characteristics of optical fiber intrusion signal, this paper designs a feature extraction implementation framework based on DSP platform. This paper uses the modularization idea to divide the function, mainly including the data synchronization and DSP data storage between the component DSP and the host computer data interaction logic DSP. The method and realization of the fiber optic intrusion signal preprocessing and feature extraction proposed in this paper are analyzed by using the field actual data. Verification was carried out.
【學位授予單位】：北方工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP277

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本文編號：1645795