基于固有时间尺度分解的电台瞬态特征提取

Intrinsic time-scale decomposition based approach for radio transient character extraction

摘要:

在实际接收到的电台瞬态信号通常都表现出非平稳性或非线性，可以通过对此类信号的瞬时参数的估计达到信号检测及电台个体识别的目的。然而，利用传统的方法如希尔伯特变换或小波变换对这些非平稳信号进行处理时常常会遇到困难。提出一种基于固有时间尺度分解的非平稳信号瞬时参数提取及电台个体识别方法，仿真试验结果证明了该方法的有效性。

Abstract:

Practical signals such as radio transient 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 radio stations and aerospace targets. However, conventional methods such as wavelet and Wigner-Ville distribution(WVD) methods are difficult to deal with these transient signals when the signal is not stationary. To provide more efficient approach for estimating instantaneous parameters of non-stationary and Specific Emitter Identification(SEI) with high accuracy, a method based on Intrinsic Time-scale Decomposition(ITD) for analytic signal is proposed in this paper. The simulation results are presented to demonstrate the effectiveness of this novel method.