矿井塌方体下超宽带生命信号的检测算法
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摘要
针对矿井塌方体下超宽带信号检测生命特征受杂波干扰无法有效提取的问题,文中基于多普勒效应理论,建立矿井下超宽带回波信号的模型。结合生命信号多奇异值的特征,提出采用SVD与小波变换联合算法,滤除杂波子空间。选用db4的小波基函数对滤除杂波后的回波信号进行7尺度的分解和重构,从第7层低频分量和第6层高频分量中所提取的目标生命体呼吸、心跳的波形信息,采用FFT变换进行定量分析。结果表明,该联合算法能够准确有效的提取和分离超宽带回波信号中的呼吸、心跳波形,可以为矿井塌方后进行有效的生命搜救提供理论依据。
The paper is aimed at addressing the failure to effectively extract human body life sign as occurs when ultra-wideband used to detect life signs is subjected to clutter interference under a mine collapse. The study drawing on the doppler effect theory consists of developing the model of ultra-wideband echo detection under the mine landslide; producing a novel joint algorithm of SVD and wavelet transform,combined with the characteristics of multiple singular values of life signals; thereby filtering out the clutter subspace; decomposing and reconstructing the echo signal after filtering the clutter using the wavelet basis function of db4; ultimately extracting the waveform information of the target living body's breathing and heartbeat from The 7 th layer low frequency component and the 6 th layer high-frequency components;and FFT-transforming the waveform information for quantitatively analyzing the spectrum. The results show that the joint algorithm capable of accurate and effective extraction and separation of the respiratory and heartbeat waveforms in UWB echo signals could provide a theoretical basis for effective life search and rescue after a mine collapse.
The paper is aimed at addressing the failure to effectively extract human body life sign as occurs when ultra-wideband used to detect life signs is subjected to clutter interference under a mine collapse. The study drawing on the doppler effect theory consists of developing the model of ultra-wideband echo detection under the mine landslide; producing a novel joint algorithm of SVD and wavelet transform,combined with the characteristics of multiple singular values of life signals; thereby filtering out the clutter subspace; decomposing and reconstructing the echo signal after filtering the clutter using the wavelet basis function of db4; ultimately extracting the waveform information of the target living body's breathing and heartbeat from The 7 th layer low frequency component and the 6 th layer high-frequency components;and FFT-transforming the waveform information for quantitatively analyzing the spectrum. The results show that the joint algorithm capable of accurate and effective extraction and separation of the respiratory and heartbeat waveforms in UWB echo signals could provide a theoretical basis for effective life search and rescue after a mine collapse.
引文
[1]郭继坤,王保生,郝维来,等.基于超宽带信号的矿井塌方体下生命特征的检测方法[J].黑龙江科技大学学报,2017,27(1):73-76.
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[14]程尧.非接触式生命体征雷达探测系统及天线设计[D].南京:南京理工大学,2014.
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[16]Zhang Y.,An interference suppression technique for life detection using 5.75-and 35-GHz dual-frequency continuous-wave radar[J].IEEE Geoscience and Remote Sensing Letters,2015,12(3),482-486.
[17]Ishihara S,Kobayashi H.Development of a distributed disaster data and human life sign probe system[J].IEEE International Conference on Industrial Technology,2016,16(5):1878-1883.
[18]Stephane G,Mallat.A Theory for multiresolution signal decomposition:The Wavelet Representation[J].IEEE Transactions on Pattern analysis and Machine intelligence,2014,25(7):674-692.
[19]Jiang Sh Y,Gao T Ch,Liu X CH.The simulation and error analysis of raindrop size distribution obtained by micro rain radar[J].Instrumentation,2015(3):43-54.
[20]Wu SH Y,Yao S Q,Tan K.Study on a novel UWB linear array human respiration model and detection method[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(1):125-140.
[2]杨秀芳,张伟,王若嘉,等.基于小波变换的雷达生命信号提取方法[J].光子学报,2013,42(11):1370-1375.
[3]杨秀芳,张伟,杨宇祥.基于提升小波变换的雷达生命信号去噪技术[J].光学学报,2014,34(3):280-283.
[4]娄鉴.超宽带生命探测雷达[D].杭州:浙江大学,2013:9-43.
[5]王亮.基于超宽带雷达生命探测算法研究[D].杭州:浙江大学,2017:31-40.
[6]丁鹭飞,耿富录,陈建春.雷达原理[M].第5版.北京:电子工业出版社,2014.
[7]Hu P,Dai W.Enhancing fault tolerance based on Hadoop cluster[J].International Journal of Database Theory and Application,2014,7(1):37-48.
[8]Zhang Z,Li Y.Analysis and study on under cloud computing multiple sets of fault tolerance strategy architecture of MapReduce[J].Microelectronics and Computer,2014,31(1):52-55.
[9]王明,张春熹,伊小素.基于自适应心跳算法的分布式系统故障检测器[J].北京航空航天大学学报,2013(7):952-956.
[10]唐良勇,赵恒,张亚菊.基于WA-EMD算法的脉冲式超宽带雷达多目标生命体征检测[J].南京理工大学学报,2017,41(2):198-206.
[11]郑鑫.基于小波变换的心电信号去噪识别的研究与实现[D].北京:北京邮电大学,2016:44-47.
[12]安强,李钊,吕昊,等.UWB搜救生物雷达分段时窗探测系统的实现[J].中国医疗设备,2015(11):18-21.
[13]刘苗.超宽带系统中多普勒时移的跟踪与检测及K波段TR组件的设计研究[D].杭州:浙江大学,2013.
[14]程尧.非接触式生命体征雷达探测系统及天线设计[D].南京:南京理工大学,2014.
[15]张亚菊.基于经验模式分解的非接触式生命体征信号处理方法研究[D].南京:南京理工大学,2015.
[16]Zhang Y.,An interference suppression technique for life detection using 5.75-and 35-GHz dual-frequency continuous-wave radar[J].IEEE Geoscience and Remote Sensing Letters,2015,12(3),482-486.
[17]Ishihara S,Kobayashi H.Development of a distributed disaster data and human life sign probe system[J].IEEE International Conference on Industrial Technology,2016,16(5):1878-1883.
[18]Stephane G,Mallat.A Theory for multiresolution signal decomposition:The Wavelet Representation[J].IEEE Transactions on Pattern analysis and Machine intelligence,2014,25(7):674-692.
[19]Jiang Sh Y,Gao T Ch,Liu X CH.The simulation and error analysis of raindrop size distribution obtained by micro rain radar[J].Instrumentation,2015(3):43-54.
[20]Wu SH Y,Yao S Q,Tan K.Study on a novel UWB linear array human respiration model and detection method[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(1):125-140.