SFCW生物雷达人体细粒度运动信号微多普勒特征增强方法研究
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摘要
目的本文提出了一种基于反权重系数的综合距离累积时频变换方法,增强了步进频连续波超宽带生物雷达人体细粒度运动信号的微多普勒特征。方法基于双通道步进频连续波(SFCW)雷达系统,通过将人体运动超宽带雷达信号不同距离单元信号分别进行时频变换得到各自时间-频率谱,然后根据各自相对应合理权重沿距离轴进行累积。结果基于反权重综合距离累积时频谱效果好,信号特征明显,较远距离穿墙情况下,运动信号微多普勒特征因衰减较大而较为微弱时,本方法优势十分明显。结论此法充分利用人体运动SFCW超宽带雷达信号不同距离单元信息,在保证信号特征完整性和原始性的基础上合理有效地增强运动信号时间-频谱中的微多普勒特征。
Objective A comprehensive distance accumulation time-frequency transform method based on the anti-weights factor is proposed in this study, so as to enhance the micro-Doppler signatures of the finer-grained human activity, which would be weakened dramatically due to the process of penetrating the wall and the increasing detection range, which is not conducive to effective analysis and accurate recognition of finer-grained human activity. Methods The corresponding time-frequency representation(TFR) was obtained by performing a time-frequency transform in each range based on the stepped frequency continuous wave radar signal of the finer-grained human activity. Results A comprehensive time-frequency representation was obtained from the summation of the different TFRs based on their corresponding weight along the range axis. Consequently, the time-micro-Doppler signature reflected in the time-frequency representation was enhanced significantly. Conclusion The technique guaranteed the integrity and primitive characteristics of the signal and laid a good foundation for analyzing effectively and recognizing accurately the finer-grained human activity when detecting through-wall or remotely.
Objective A comprehensive distance accumulation time-frequency transform method based on the anti-weights factor is proposed in this study, so as to enhance the micro-Doppler signatures of the finer-grained human activity, which would be weakened dramatically due to the process of penetrating the wall and the increasing detection range, which is not conducive to effective analysis and accurate recognition of finer-grained human activity. Methods The corresponding time-frequency representation(TFR) was obtained by performing a time-frequency transform in each range based on the stepped frequency continuous wave radar signal of the finer-grained human activity. Results A comprehensive time-frequency representation was obtained from the summation of the different TFRs based on their corresponding weight along the range axis. Consequently, the time-micro-Doppler signature reflected in the time-frequency representation was enhanced significantly. Conclusion The technique guaranteed the integrity and primitive characteristics of the signal and laid a good foundation for analyzing effectively and recognizing accurately the finer-grained human activity when detecting through-wall or remotely.
引文
[1]Fairchild DP,Narayanan RM.Micro-doppler radar classification of human motions under various training scenarios[C].SPIEDefense,Security,and Sensing.International Society for Optics and Photonics,2013:873407-873411.
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[6]Chen VC.Analysis of radar micro-Doppler with time-frequency transform[C].Statistical Signal and Array Processing,2000.Proceedings of the Tenth IEEE Workshop on.IEEE,2000:463-466.
[7]Chen VC,Li F,Ho SS,et al.Micro-Doppler effect in radar:phenomenon,model,and simulation study[J].IEEE T Aero Elec Sys,2006,42(1):2-21.
[8]Kim Y,Ling H.Human activity classification based on microDoppler signatures using an artificial neural network[C].Antennas and Propagation Society International Symposium,2008.AP-S 2008.IEEE.IEEE,2008:1-4.
[9]Kim Y,Ling H.Human activity classification based on microDoppler signatures using a support vector machine[J].IEEE TGeosci Remote S,2009,47(5):1328-1337.
[10]Lai CP,Ruan Q,Narayanan RM.Hilbert-Huang Transform(HHT)analysis of human activities using through-wall noise radar[C].Signals,Systems and Electronics,2007.ISSSE'07.International Symposium on.IEEE,2007:115-118.
[11]Chen VC.Joint time-frequency analysis for radar signal and imaging[C].Geoscience and Remote Sensing Symposium,2007.IGARSS 2007.IEEE International.IEEE,2007:5166-5169.
[2]Bryan JD,Kwon J,Lee N,et al.Application of ultra-wide band radar for classification of human activities[J].IET Radar Sonar Navig,2012,6(3):172-179.
[3]Fairchild DP,Narayanan RM.Classification and modeling of human activities using empirical mode decomposition with S-band and millimeter-wave micro-Doppler radars[C].SPIEDefense,Security,and Sensing.International Society for Optics and Photonics,2012:83610X-15.
[4]Lyonnet B,Ioana C,Amin MG.Human gait classification using micro Doppler time-frequency signal representations[C].Radar Conference,2010 IEEE.IEEE,2010:915-919.
[5]Smith GE,Ahmad F,Amin MG.Micro-Doppler processing for ultra-wideband radar data[C].SPIE Defense,Security,and Sensing.International Society for Optics and Photonics,2012:83610L-83610L-10.
[6]Chen VC.Analysis of radar micro-Doppler with time-frequency transform[C].Statistical Signal and Array Processing,2000.Proceedings of the Tenth IEEE Workshop on.IEEE,2000:463-466.
[7]Chen VC,Li F,Ho SS,et al.Micro-Doppler effect in radar:phenomenon,model,and simulation study[J].IEEE T Aero Elec Sys,2006,42(1):2-21.
[8]Kim Y,Ling H.Human activity classification based on microDoppler signatures using an artificial neural network[C].Antennas and Propagation Society International Symposium,2008.AP-S 2008.IEEE.IEEE,2008:1-4.
[9]Kim Y,Ling H.Human activity classification based on microDoppler signatures using a support vector machine[J].IEEE TGeosci Remote S,2009,47(5):1328-1337.
[10]Lai CP,Ruan Q,Narayanan RM.Hilbert-Huang Transform(HHT)analysis of human activities using through-wall noise radar[C].Signals,Systems and Electronics,2007.ISSSE'07.International Symposium on.IEEE,2007:115-118.
[11]Chen VC.Joint time-frequency analysis for radar signal and imaging[C].Geoscience and Remote Sensing Symposium,2007.IGARSS 2007.IEEE International.IEEE,2007:5166-5169.