發(fā)布時間：2018-04-15 20:53

  本文選題：防入侵 + 目標(biāo)識別��； 參考：《吉林大學(xué)》2015年碩士論文

【摘要】：目前常用的周界入侵防御系統(tǒng)的探測傳感器主要有聲、地震動、被動紅外、磁、電、視頻等傳感器，但是由于很多物理量容易受到外界環(huán)境的影響，導(dǎo)致基于該類傳感器系統(tǒng)的性能難以達(dá)到較高的要求，地震檢波器具有受多普勒效應(yīng)和環(huán)境影響最小的優(yōu)勢，因此被廣泛應(yīng)用于周界入侵防御系統(tǒng)中。但目前市場上常用的地震檢波器自然頻率較高且靈敏度較低，探測距離較近的同時會丟失部分目標(biāo)識別需要的低頻成分，因此有必要尋找一種高靈敏度的超低頻地震檢波器。MET地震檢波器具有靈敏度高、自然頻率可達(dá)1Hz的優(yōu)勢，因此本文開發(fā)基于MET地震檢波器的周界入侵防御系統(tǒng)，同時選取目前最常用的傳統(tǒng)地震檢波器——JF-20DX-10Hz作為對比探測傳感器。本文的主要研究內(nèi)容為： 第一，研究了地震動信號的基本理論，分析了目標(biāo)運(yùn)動時地震波的產(chǎn)生及傳播機(jī)理，對包含地震波主要能量的瑞雷波的特性進(jìn)行詳細(xì)闡述，為后續(xù)數(shù)據(jù)處理做理論基礎(chǔ)。 第二，對MET地震檢波器的結(jié)構(gòu)和工作原理進(jìn)行了介紹，并設(shè)計(jì)一系列實(shí)驗(yàn)對其實(shí)際性能進(jìn)行測試，分別為自身噪聲測試、靈敏度測試、相頻特性測試，自身噪聲測試使用目前地震領(lǐng)域的國際標(biāo)準(zhǔn)測試其噪聲加速度功率譜密度和速度功率譜密度，靈敏度和相頻特性測試使用超低頻振動臺對其進(jìn)行掃頻測試得到其靈敏度及相頻特性，通過上述三個實(shí)驗(yàn)證明MET地震檢波器的實(shí)際性能符合其標(biāo)定指標(biāo)。 第三，，研究了基于小波包組合BP神經(jīng)網(wǎng)絡(luò)的目標(biāo)識別算法。首先使用過零分析和峰度分析在時域上對地震動信號進(jìn)行分析，使用功率譜分析在頻域上對信號進(jìn)行分析；然后對小波分析的原理進(jìn)行介紹，并介紹了常用的小波函數(shù)，學(xué)習(xí)小波去噪算法的原理，學(xué)習(xí)小波分析的改進(jìn)算法——小波包分析在特征提取方面的應(yīng)用；接著對BP神經(jīng)網(wǎng)絡(luò)的原理進(jìn)行介紹，學(xué)習(xí)其網(wǎng)絡(luò)結(jié)構(gòu)、訓(xùn)練過程及其在目標(biāo)識別方面的應(yīng)用；最后確立小波包組合BP神經(jīng)網(wǎng)絡(luò)的目標(biāo)識別算法模型，確定小波包特征提取算法，構(gòu)建BP神經(jīng)網(wǎng)絡(luò)并對網(wǎng)絡(luò)的訓(xùn)練流程進(jìn)行詳細(xì)闡述。 第四，開發(fā)周界入侵防御系統(tǒng)軟件，軟件使用NI LabVIEW進(jìn)行開發(fā)，編寫良好的人機(jī)交互界面實(shí)現(xiàn)采集數(shù)據(jù)的顯示、保存及處理。使用NI公司的TDMS文件格式進(jìn)行數(shù)據(jù)保存；使用LabVIEW調(diào)用MATLAB的方式實(shí)現(xiàn)目標(biāo)信號的特征提取和BP神經(jīng)網(wǎng)絡(luò)的訓(xùn)練及識別，將識別結(jié)果以文字的方式直接顯示在系統(tǒng)軟件主界面上。為了進(jìn)一步提高目標(biāo)識別的準(zhǔn)確率，增加離線分析模塊對采集數(shù)據(jù)進(jìn)行進(jìn)一步分析。 外場實(shí)驗(yàn)結(jié)果表明，相比JF-20DX-10Hz地震檢波器，基于MET地震檢波器的周界入侵防御系統(tǒng)具有更高的識別精度和識別距離。
[Abstract]:At present, the detection sensors of the perimeter intrusion Prevention system are mainly acoustic, ground motion, passive infrared, magnetic, electric, video and other sensors. However, because many physical quantities are easily affected by the external environment,Because the performance of the sensor system based on this kind of sensor system is difficult to meet the higher requirements, the geophone has the advantage of least influence by Doppler effect and environment, so it is widely used in perimeter intrusion prevention system.However, the seismic geophone, which is commonly used in the market, has high natural frequency and low sensitivity, and it will lose some of the low-frequency components needed for target recognition while the detection distance is close.Therefore, it is necessary to find a highly sensitive ultra-low frequency geophone. Met geophone has the advantages of high sensitivity and natural frequency up to 1Hz. Therefore, a perimeter intrusion prevention system based on MET geophone is developed in this paper.At the same time, JF-20DX-10 Hz, the most commonly used geophone, is chosen as the contrast detection sensor.The main contents of this paper are as follows:Firstly, the basic theory of ground motion signal is studied, the generation and propagation mechanism of seismic wave are analyzed, and the characteristics of Rayleigh wave, which contains the main energy of seismic wave, are described in detail, which is the theoretical basis for the subsequent data processing.Secondly, the structure and working principle of MET seismograph are introduced, and a series of experiments are designed to test its actual performance, which are noise test, sensitivity test and phase frequency characteristic test.Self noise testing uses current international standards in the seismic field to test the noise acceleration power spectral density and the velocity power spectrum density.The sensitivity and phase frequency characteristics of MET geophone are measured by scanning frequency with ultra-low frequency vibration table. The results show that the actual performance of MET seismograph accords with its calibration index.Thirdly, the target recognition algorithm based on wavelet packet combination BP neural network is studied.At first, zero-crossing analysis and kurtosis analysis are used to analyze the ground motion signal in time domain, and power spectrum analysis is used to analyze the signal in frequency domain, then the principle of wavelet analysis is introduced, and the commonly used wavelet function is introduced.Learning the principle of wavelet denoising algorithm, learning the application of wavelet packet analysis in feature extraction, then introducing the principle of BP neural network, learning its network structure.The training process and its application in target recognition. Finally, the model of target recognition algorithm based on wavelet packet combination BP neural network is established, the feature extraction algorithm of wavelet packet is determined, the BP neural network is constructed and the training flow of the network is described in detail.Fourthly, the software of perimeter intrusion prevention system is developed. The software is developed with NI LabVIEW, and a good man-machine interface is written to display, save and process the collected data.The TDMS file format of NI company is used to save the data, the feature extraction of target signal and the training and recognition of BP neural network are realized by LabVIEW calling MATLAB, and the recognition result is displayed directly on the main interface of the system software in the way of text.In order to further improve the accuracy of target recognition, an off-line analysis module is added to further analyze the collected data.The results of field experiments show that the perimeter intrusion prevention system based on MET geophone has higher recognition precision and distance than JF-20DX-10Hz geophone.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P631.436;TN911.7

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