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小波包特征提取算法在信号分析中应用 被引量:2

Application of Wavelet Packet Transform in Analysis of Signals
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摘要 小波包算法能把低频和高频分开,比其他算法更容易分析信号的频谱特征。小波包算法卓越的时频分辨率,及其自适应性,有助于目标的准确定位,在对浅层埋地目标的检测中有较好的应用前景。 Wavelet packet analysis has ability to divide the low and high spectrum, wavelet packet analysis is more useful in analyzing signals. Wavelet packet analysis can adapt itself, and improve the timer resolution, so we can position the target accuratly. Processing result of field data show that this method is effective
机构地区 华东师范大学
出处 《电子测量技术》 2005年第5期27-28,共2页 Electronic Measurement Technology
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参考文献3

  • 1王首勇,朱光喜,唐远炎.应用最优小波包变换的特征提取方法[J].电子学报,2003,31(7):1035-1038. 被引量:50
  • 2WON G KM, et al. Wavelet packet division multiplexing and wavelet packet design under timing error effects[J]. IEEE Trans Signal Processing, 1997, 45 (12):2877-2890.
  • 3DAUBECH IES I. The wavelet transform time-frequency localization and signal analysis [J]. IEEE Trans on Info Theory, 1990, IT236 (9). 961-1005.

二级参考文献6

  • 1R R Coifman, M V W Hauser. Entropy based algorithms for best basis selection [J]. IEEE Trans, 1992, IT-38(3) :713 - 718.
  • 2G Antonini, A Orlandi. Wavelet packet-based EMI signal processing and source identification [ J ]. IEEE Trans, 2001, EC-43 ( 2 ) : 140 -148.
  • 3H Liang, I Hartimo. A feature extraction algorithm based on wavelet packet decompo, sition for heart sound signals [ A ]. Proc of IEEE-SP Inter. Symp [ C ]. USA: IEEE, 1998.93 - 96.
  • 4N Satio, R Coifman. On local orthonormal bases for classification and regression [ A]. Prec Qf IEEE ICASSP [ C]. USA: IEEE, 1995.1529 -1532.
  • 5N Satio, R Coifman. Local discriminant bases and their applications[J]. Mathematical Imaging and Vision, 1995,5(4) :337 - 358.
  • 6R Samkaya,J H L Hansen. High resolution speech feature parameterization for monophone-based stressed speech recognition [J]. IEEE SP Letters.2000,7(7) : 182 - 185.

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