Hilbert包络谱的熵特征在辐射源个体识别中的应用

针对基于时频统计参数的特征提取方法存在测量准确性要求高，特征维数大的问题，提出了一种基于Hilbert包络谱的熵特征。利用Hilbert法对信号提取包络谱后，求解包络谱的信息熵和指数熵特征，并联合包络方差识别辐射源个体。在仿真实验中对比功率谱熵特征识别正确率显著提高；对QAM、ASK以及频率成分适量的信号识别率在3 dB左右达到80%以上。该算法具有一定高效性和鲁棒性。此外应用特征于FM手持机信号，实测结果表明，算法具有一定实用性。

Application of entropy characteristics of Hilbert envelope spectrum in specific emitter identification

As specific emitter identification belongs to unintentional modulation recognition, the information of which will be reflected in the envelope of signal. The requirement of the measurement accuracy is high and the dimension of the features is large for the feature extraction method based on time-frequency statistical parameters. Thus, this paper proposed a method based on entropy characteristics of envelope spectrum to solve above problems. It extracted the envelope spectrum from the signal by Hilbert method. Then, it obtained the Shannon entropy and exponential entropy characteristics of the envelope spectrum, while identified the emitters by combining with the variance of envelope. In the simulation experiment, it improved the accuracy significantly compared with features from power spectrum entropy. The signal recognition rate is above 80% at 3 dB for signals of QAM, ASK and signals with fewer frequency components. It shows that the algorithm is efficient and robust. In addition, being applied to the signal from FM handheld radios, the algorithm proposed possess the characteristic of practicability.