<progress id="blzj5"></progress>
<menuitem id="blzj5"><del id="blzj5"><address id="blzj5"></address></del></menuitem><thead id="blzj5"></thead>
<menuitem id="blzj5"><dl id="blzj5"></dl></menuitem>
<thead id="blzj5"><dl id="blzj5"></dl></thead>
<var id="blzj5"><dl id="blzj5"></dl></var>
<listing id="blzj5"><dl id="blzj5"><noframes id="blzj5">
<thead id="blzj5"><dl id="blzj5"></dl></thead><listing id="blzj5"></listing><listing id="blzj5"></listing>
<menuitem id="blzj5"></menuitem>
<ins id="blzj5"></ins>
<thead id="blzj5"><del id="blzj5"><th id="blzj5"></th></del></thead><listing id="blzj5"></listing>
<thead id="blzj5"></thead>
李成華,程博,江小平.基于時頻能量比的入侵事件識別方法[J].中南民族大學學報自然科學版,2019,(2):258-264
基于時頻能量比的入侵事件識別方法
Intrusion event recognition method based on time-frequency energy ratio
  
DOI:10.12130/znmdzk.20190220
中文關鍵詞: 入侵事件識別  挖掘  人步行  時頻能量比  SVM
英文關鍵詞: Intrusion event identification  digging  human walking  Time-frequency energy ratio  SVM
基金項目:湖北省自然科學基金項目(2017CFB874);中央高?;究蒲袠I務費專項資助項目(CZY17001)
作者單位
李成華,程博*,江小平 中南民族大學 電子信息工程學院,智能無線通信湖北省重點實驗室,武漢430074 
摘要點擊次數: 180
全文下載次數: 178
中文摘要:
      針對挖掘入侵事件與人步行等干擾事件的識別問題,提出一種基于時頻能量比的識別方法。利用時域的節律特征以及信號包絡的時域沖擊特征,剔除如車輛路過、自然環境干擾等事件,留下挖掘和人步行事件。對于挖掘和人步行事件的識別,首先,對事件信號進行時域窗分割;其次,將時域分割后的每個子信號輸入到一組窄帶濾波器中,并計算每個濾波器輸出信號與輸入的時域子信號的能量比值,得到信號的時頻能量比特征。最后,利用SVM作為分類器,進行分類實驗。實驗表明,該方法提取的時頻特征所包含的冗余特征數據量小,分類所需的時間短,分類識別的準確率約為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%.
查看全文   查看/發表評論  下載PDF閱讀器
關閉
秒速赛车开奖查询结果