基于时频能量比的入侵事件识别方法
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摘要
针对挖掘入侵事件与人步行等干扰事件的识别问题,提出一种基于时频能量比的识别方法.利用时域的节律特征以及信号包络的时域冲击特征,剔除如车辆路过、自然环境干扰等事件.留下挖掘和人步行事件.对于挖掘和人步行事件的识别,首先,对事件信号进行时域窗分割;其次,将时域分割后的每个子信号输入到一组窄带滤波器中,并计算每个滤波器输出信号与输入的时域子信号的能量比值,得到信号的时频能量比特征.最后,利用SVM作为分类器,进行分类实验.实验表明,该方法提取的时频特征所包含的冗余特征数据量小,分类所需的时间短,分类识别的准确率约为94%.
In order to identify digging intrusion event and interference events such as people walking,a recognition method based on time-frequency energy ratio is proposed. Using the rhythm characteristics of the time-domain and the time-domain impact characteristics of the signal envelope,events such as vehicle passing and natural environment interference are eliminated,digging and human walking events are remained. For identifying digging and human walking events,first,timedomain window segmentation is performed on the event signal. Secondly,each sub-signal after time domain segmentation is input into a set of narrow-band filters,and the energy ratio of each filter output signal and input are calculated,then get time-frequency energy ratio characteristic of the signal. Finally,the SVM is used as a classifier. The experimental results show that the time-frequency features extracted by the method contain small amount of redundant feature data,short time required for classification,the accuracy of classification recognition is about 94%.
In order to identify digging intrusion event and interference events such as people walking,a recognition method based on time-frequency energy ratio is proposed. Using the rhythm characteristics of the time-domain and the time-domain impact characteristics of the signal envelope,events such as vehicle passing and natural environment interference are eliminated,digging and human walking events are remained. For identifying digging and human walking events,first,timedomain window segmentation is performed on the event signal. Secondly,each sub-signal after time domain segmentation is input into a set of narrow-band filters,and the energy ratio of each filter output signal and input are calculated,then get time-frequency energy ratio characteristic of the signal. Finally,the SVM is used as a classifier. The experimental results show that the time-frequency features extracted by the method contain small amount of redundant feature data,short time required for classification,the accuracy of classification recognition is about 94%.
引文
[1]KANNIGA E,RATHNAM K S R,YADAV A K.Wireless based target detection and object identification using seismic and pir sensors[J].Middle East Journal of Scientific Research,2014,20(3):377-380.
[2]VIRENDRA,SHETE V,UKUNDE N.Intelligent embedded video monitoring system for home surveillance[C]//IEEE.International Conference on Inventive Computation Technologies.Coimbatore:IEEE,2017:1-4.
[3]HUANG Y.The design and implementation on a new generation of remote network video surveillance system[J].Microcomputer Information,2010,2:94-97.
[4]ANCHAL S,MUKHOPADHYAY B,KAR S.Predicting gender from footfalls using a seismic sensor[C]//IEEE.International Conference on Communication Systems and Networks.Bangalore:IEEE,2017:47-54.
[5]ALDALAHMEH S,HAMDAN A,GHOGHO M,et al.Enhanced-Range intrusion detection using Pyroelectric Infrared Sensors[C]//IEEE.Sensor Signal Processing for Defence.Edinburgh:IEEE,2016.
[6]ZHOU Q,LI B,LIU H,et al.A microphone based vibration sensor for UGS applications[J].IEEE Transactions on Industrial Electronics,2017:6565-6572.
[7]LEVY R,MORAS J,PANNETIER B.Vibrating beam MEMS seismometer for footstep and vehicle detection[J].IEEE Sensors Journal,2017:7306-7310.
[8]MUKHOPADHYAY B,ANCHAL S,KAR S.Detection of an intruder and prediction of his state of motion by using seismic sensor[J].IEEE Sensors Journal,2017:703-712.
[9]SCHUMER S.Analysis of human footsteps utilizing multiaxial seismic fusion[C]//IEEE.International Conference on Acoustics,Speech and Signal Processing.Prague:IEEE,2011:697-700.
[10]PARK H O,DIBAZAR A A,BERGER T W.Cadence analysis of temporal gait patterns for seismic discrimination between human and quadruped footsteps.[C]//IEEE.International Conference on Acoustics,Speech and Signal Processing.Taipei:IEEE,2009:1749-1752.
[11]NEILD S A,MCFADDEN P D,WILLIAMS M S.A review of time-frequency methods for structural vibration analysis[J].Engineering Structures,2003,25(6):713-728.
[12]HE C,ZWEIGH G.Adaptive two-band spectral subtraction with multi-window spectral estimation[C]//IEEE.1999 IEEE International Conference on Acoustics,Speech,and Signal Processing.Phoenix:IEEE Computer Society,1999:793-796.
[13]SANG M J,PARK P G.Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs[J].Electronics Letters,2013,49(20):1270-1272.
[14]ARABLOUEI R,WERNER S,DOGANCAY K.Analysis of the gradient-descent total least-squares adaptive filtering algorithm[J].IEEE Transactions on Signal Processing,2014,62(5):1256-1264.
[15]DUAN J,CHENG J,WWNG Y,et al.Automatic identification technology of microseismic event based on STA/LTA algorithm[J].Coal Geology&Exploration,2015.
[16]CAO C,PENG Z,LIU Q,et al.Personnel and vehicle signal sensing based on seismic detection[C]//International Conference on Intelligent Control&Information Processing.Dalian:IEEE,2012.
[2]VIRENDRA,SHETE V,UKUNDE N.Intelligent embedded video monitoring system for home surveillance[C]//IEEE.International Conference on Inventive Computation Technologies.Coimbatore:IEEE,2017:1-4.
[3]HUANG Y.The design and implementation on a new generation of remote network video surveillance system[J].Microcomputer Information,2010,2:94-97.
[4]ANCHAL S,MUKHOPADHYAY B,KAR S.Predicting gender from footfalls using a seismic sensor[C]//IEEE.International Conference on Communication Systems and Networks.Bangalore:IEEE,2017:47-54.
[5]ALDALAHMEH S,HAMDAN A,GHOGHO M,et al.Enhanced-Range intrusion detection using Pyroelectric Infrared Sensors[C]//IEEE.Sensor Signal Processing for Defence.Edinburgh:IEEE,2016.
[6]ZHOU Q,LI B,LIU H,et al.A microphone based vibration sensor for UGS applications[J].IEEE Transactions on Industrial Electronics,2017:6565-6572.
[7]LEVY R,MORAS J,PANNETIER B.Vibrating beam MEMS seismometer for footstep and vehicle detection[J].IEEE Sensors Journal,2017:7306-7310.
[8]MUKHOPADHYAY B,ANCHAL S,KAR S.Detection of an intruder and prediction of his state of motion by using seismic sensor[J].IEEE Sensors Journal,2017:703-712.
[9]SCHUMER S.Analysis of human footsteps utilizing multiaxial seismic fusion[C]//IEEE.International Conference on Acoustics,Speech and Signal Processing.Prague:IEEE,2011:697-700.
[10]PARK H O,DIBAZAR A A,BERGER T W.Cadence analysis of temporal gait patterns for seismic discrimination between human and quadruped footsteps.[C]//IEEE.International Conference on Acoustics,Speech and Signal Processing.Taipei:IEEE,2009:1749-1752.
[11]NEILD S A,MCFADDEN P D,WILLIAMS M S.A review of time-frequency methods for structural vibration analysis[J].Engineering Structures,2003,25(6):713-728.
[12]HE C,ZWEIGH G.Adaptive two-band spectral subtraction with multi-window spectral estimation[C]//IEEE.1999 IEEE International Conference on Acoustics,Speech,and Signal Processing.Phoenix:IEEE Computer Society,1999:793-796.
[13]SANG M J,PARK P G.Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs[J].Electronics Letters,2013,49(20):1270-1272.
[14]ARABLOUEI R,WERNER S,DOGANCAY K.Analysis of the gradient-descent total least-squares adaptive filtering algorithm[J].IEEE Transactions on Signal Processing,2014,62(5):1256-1264.
[15]DUAN J,CHENG J,WWNG Y,et al.Automatic identification technology of microseismic event based on STA/LTA algorithm[J].Coal Geology&Exploration,2015.
[16]CAO C,PENG Z,LIU Q,et al.Personnel and vehicle signal sensing based on seismic detection[C]//International Conference on Intelligent Control&Information Processing.Dalian:IEEE,2012.