Chinese Space Science and Technology ›› 2023, Vol. 43 ›› Issue (4): 126-136.doi: 10.16708/j.cnki.1000-758X.2023.0061

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Space emitter fine feature identification based on multi-domain fusion

WANG Xiaohan1,2,YAN Yi1,FAN Yanan1,LI Xue1,MOU Jiao1   

  1. 1 National Space Science Center,CAS,Beijing 100190,China
    2 University of Chinese Academy of Sciences,Beijing 100049,China
  • Published:2023-08-25 Online:2023-07-18

Abstract: Emitter identification mainly focuses on individual differences of emitters,and obtains characteristic parameters related to equipment hardware on emitter signals through signal processing means,so as to master the model and working status of emitter equipment.As communication signals become more and more complex,feature identification in a single domain cannot fully reflect the fine differences of signals,and there is a large amount of data redundancy in direct identification of signal processing results,resulting in poor identification effect.In order to improve the identification effect of space emitter,an emitter identification method was proposed based on the multi-domain feature fusion.By perfroming Hilbert-Huang transform and higher order spectrum analysis of the signal,the mean,variance,kurtosis,skewness and information entropy of the teansformed results are exteacted,SVM classifier is used to classify emitters and realize the fine feature identification of multidomain fusion.The simulation results show that the accuracy of multidomain feather fusion method can reach 95.44% under the SNR of 20dB,and that the accuracy is improved by 9.41% compared with the traditional classification method based on marginal spectrum information entropy.Compared with the classification and identification methods based on HHT marginal spectrum,marginal spectrum information entropy,bispectral projection,bispectral rectangular integral and fourth-order cumulant slice spectrum,the identification effect of the proposed method is improved.

Key words: space communication, feature fusion, space emitter identification, Hilbert-Huang transform, higher order spectra