【摘 要】
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Communication behavior recognition is an issue with increasingly importance in the anti-terrorism and national defense area.However,the sensing data obtained in actual environment is of-ten not sufficient to accurately analyze the commu-nication behavior.
【机 构】
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The College of Communications Engineering,Army Engineering University,Nanjing 210007,China;School of
论文部分内容阅读
Communication behavior recognition is an issue with increasingly importance in the anti-terrorism and national defense area.However,the sensing data obtained in actual environment is of-ten not sufficient to accurately analyze the commu-nication behavior.Traditional means can hardly uti-lize the scarce and crude spectrum sensing data cap-tured in a real scene.Thus,communication behav-ior recognition using raw sensing data under small-sample condition has become a new challenge.In this paper,a data enhanced communication behavior recognition (DECBR) scheme is proposed to meet this challenge.Firstly,a preprocessing method is designed to make the raw spectrum data suitable for the pro-posed scheme.Then,an adaptive convolutional neural network structure is exploited to carry out communica-tion behavior recognition.Moreover,DCGAN is ap-plied to support data enhancement,which realize com-munication behavior recognition under small-sample condition.Finally,the scheme is verified by experi-ments under different data size.The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under small-sample condition.
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