基于生物雷达的人体运动状态探测识别技术研究
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摘要
阐述了基于生物雷达的人体运动探测识别技术在反恐作战、安全防护、灾害搜救方面的重要应用优势,并从运动检测、信号分析、特征值提取和识别分类4个方面对人体运动生物雷达探测识别技术的研究现状进行了综述。最后指出了该技术亟待解决的问题及发展方向,以期为未来该技术的研究提供参考。
The significant advantages of the human activity detection and recognition technology based on bio-radar applied in the counter terrorism, the safe guarding and the disaster search and rescue were elaborated. Besides, the research status of the human activity detection and recognition technology based on bio-radar were summarized from four aspects, including the motion detection, signal analysis, feature extraction and recognition and classification. Finally, the problems and development direction of this technology were pointed out.
The significant advantages of the human activity detection and recognition technology based on bio-radar applied in the counter terrorism, the safe guarding and the disaster search and rescue were elaborated. Besides, the research status of the human activity detection and recognition technology based on bio-radar were summarized from four aspects, including the motion detection, signal analysis, feature extraction and recognition and classification. Finally, the problems and development direction of this technology were pointed out.
引文
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[37]NARAYANAN R M.Through-wall radar imaging using UWB noise waveforms[J].J Franklin I,2008,345(6):659-678.
[38]LU B,SONG Q,ZHOU Z,et al.A SFCW radar for through wall imaging and motion detection[C]//Proceedings of 2011European Radar Conference(Eu RAD),Oct 12-14,2011,Manchester.USA:IEEE,2011:325-328.
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[41]GABOR D.Theory of communication.Part 1:The analysis of information[J].Journal of the Institution of Electrical Engineers-Part III:Radio and Communication Engineering,1946,93(26):429-441.
[42]TIVIVE F H C,BOUZERDOUM A,AMIN M G.A human gait classification method based on radar Doppler spectrograms[J].EURASIP J Adv Sig Pr,2010,2010:12.
[43]SMITHG E,AHMAD F,AMIN M G.Micro-Doppler processing for ultra-wideband radar data[C]//Proceeding of SPIE 8361,Radar Sensor Technology XVI,May 1,2012,Baltimore,Maryland.USA:SPIE,2012:83610L-836 10L-10.
[44]BIRSEL A E,BILLUR B.Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals[J].Sensors,2011,11(2):1 721-1 743.
[45]TIAN Y,NING C,ZHANG X.Characteristic study of target micro-motion based on the wavelet analysis[C]//Proceedings of Green Computing and Communications(Green Com)of2013 IEEE and Internet of Things(i Things/CPSCom),IEEE Interna-tional Conference on and IEEE Cyber and Physical and Social Computing,Aug 20-23,2013,Beijing.USA:IEEE,2013:1 571-1 575.
[46]SERIR A,BOUHAFSI Y.Micro-Doppler radar signature classification by time-frequency and time-scale analysis[C]//Proceedings of 2012 11th International Conference on Information Science,Signal Processing and their Applications(ISSPA),July 2-5,2012,Montreal,QC.USA:IEEE,2012:995-1 000.
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