微弱电信号检测方法回顾
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摘要
系统分析了微弱电信号检测的理论和技术,着重讨论了强噪声背景下微弱电信号现有检测方法的优缺点及应用.以线性和非线性为主线,将线性分析法按时域(相关检测法、锁定放大、取样积分、数字式平均、时域平均)、频域(功率谱法)以及时频域(小波变换谱分析、分数谱分析)检测方法进行梳理归类,而对于非线性分析法主要分析了高阶谱、神经网络、支持向量机、经验模式分解、混沌理论、差分振子以及随机共振方法.最后认为,多种理论与技术的结合以及借助软件技术,如LabVIEW,是弱信号检测的发展趋势.
A review on the theories and techniques for weak electrical signal detection.It put great emphasis on the weak electrical signal that was buried in strong noise.The route that was arranged in the paper based on the property of the system that was linear or nonlinear.The linear system was divided into time domain method which contained correlate detection,lock-in amplifier,sampling integrator,digital multipoint average and time averaging method,frequency domain method which pays attention to the power spectrum analyzing,and time-frequency domain method including wavelet analysis and fractional Fourier transform.The nonlinear system introduced higher order spectrum,neural networks method,chaos theory,difference resonator and stochastic resonance.The tendency of weak signal detection is towards the combined methods mentioned above and with the help of professional software such as LabVIEW.
A review on the theories and techniques for weak electrical signal detection.It put great emphasis on the weak electrical signal that was buried in strong noise.The route that was arranged in the paper based on the property of the system that was linear or nonlinear.The linear system was divided into time domain method which contained correlate detection,lock-in amplifier,sampling integrator,digital multipoint average and time averaging method,frequency domain method which pays attention to the power spectrum analyzing,and time-frequency domain method including wavelet analysis and fractional Fourier transform.The nonlinear system introduced higher order spectrum,neural networks method,chaos theory,difference resonator and stochastic resonance.The tendency of weak signal detection is towards the combined methods mentioned above and with the help of professional software such as LabVIEW.
引文
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[12]杨兴国,郭勇.基于DSP的取样数字式平均器的设计与实现[J].微计算机信息,2007,23(5):179-181.
[13]张国新,许德昌.信号时域平均算法研究[J].南方冶金学院学报,2008,29(1):42-45.
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[19]王仲生,何红.小波分析在发动机早期故障识别中的应用研究[J].西北工业大学学报,2006,24(1):68-71.
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[22]陶然,邓兵.分数阶Fourier变换在信号处理领域的研究进展[J].中国科学(E辑),2006,36(2):113-136.
[23]HALDUN M OZAKTAS.Digital computation of the frac-tional fourier transform[J].IEEE Transactions on Signal Pro-cessing,1996,44(9):2141-2150.
[24]BARSHAN B,AYRULU B.Fractional fourier transformpre-processing for neural networks and its application toobject recognition[J].Neural Networks,2002,15(1):131-140.
[25]QI L.Detection and parameter estimation of multicomponentLFM signal based on the fractional fourier transform[J].Sci-ence in China Series F:Information Sciences,2004,47(2):184-198.
[26]ZHANG Y L,ZHANG Q Y.Biomedical signal detectionbased on fractional fourier transform[C]∥Proceedings ofthe 5th International Conference on Information Technolo-gy and Application in Biomedicine.Shenzhen:[s.n.],2008:349-352.
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