李成华,程博,江小平.基于时频能量比的入侵事件识别方法[J].中南民族大学学报自然科学版,2019,(2):258-264
基于时频能量比的入侵事件识别方法
Intrusion event recognition method based on time-frequency energy ratio
  
DOI:10.12130/znmdzk.20190220
中文关键词: 入侵事件识别  挖掘  人步行  时频能量比  SVM
英文关键词: Intrusion event identification  digging  human walking  Time-frequency energy ratio  SVM
基金项目:湖北省自然科学基金项目(2017CFB874);中央高校基本科研业务费专项资助项目(CZY17001)
作者单位
李成华,程博*,江小平 中南民族大学 电子信息工程学院智能无线通信湖北省重点实验室武汉430074 
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中文摘要:
      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%。
英文摘要:
      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, time-domain 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 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%.
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