基于经验模态分解的特定发射极识别方法

Chunyun Song, Jianmin Xu, Yi Zhan
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引用次数: 23

摘要

真实的无线电和雷达信号是非平稳和非线性的时间序列,在时域上估计这些信号的瞬时参数将有助于检测和识别特定的发射器,如民用或军用无线电。然而,当信号是非平稳时,传统的小波和Wigner-Ville分布(WVD)方法难以处理这些暂态信号。为了更有效地估计非平稳信号的瞬时参数并高精度地识别特定发射极,本文提出了一种基于经验模态分解(EMD)的非平稳信号分析新方法。结果表明,EMD方法产生的瞬时频率估计比小波技术得到的估计更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method for specific emitter identification based on empirical mode decomposition
Real radio and radar signals turn out to be non-stationary and nonlinear time series, and estimating instantaneous parameters of such signals in time domain will benefit detecting and identifying specific emitters, such as civil or military radios. However, conventional methods such as Wavelet and Wigner-Ville distribution (WVD) methods are difficult to deal with these transient signals when the signal is no-stationary. To provide more efficient approach for estimating instantaneous parameters of non-stationary and identifying specific emitter with high accuracy, a new method based on empirical mode decomposition (EMD) for analyze no-stationary signal is proposed in this paper. The results show that the instantaneous frequency estimates generated by the EMD method are more accurate than those obtained by the Wavelet technique.
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