Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features

LIU Gaohui; XI Hongen

doi:10.11999/JEIT231348

Volume 46 Issue 10

Oct. 2024

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LIU Gaohui, XI Hongen. Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4044-4052. doi: 10.11999/JEIT231348

Citation:

LIU Gaohui, XI Hongen. Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4044-4052. doi: 10.11999/JEIT231348

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Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features

doi: 10.11999/JEIT231348

LIU Gaohui,
XI Hongen^,

School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Funds: The National Natural Science Foundation of China (61671375)

Received Date: 2023-12-05
Rev Recd Date: 2024-09-05

Available Online: 2024-09-11

Publish Date: 2024-10-30

Abstract

Abstract

Aiming at the difficulties in extracting fingerprint features from communication emitters and the low recognition rate of single features, considering the nonlinear and non-stationary characteristics of subtle features of communication emitters, this paper proposes an individual identification method for communication emitters based on improved variational mode decomposition and multiple features. Firstly, in order to obtain the optimal combination of decomposition levels and penalty factors for variational mode decomposition, the variational modal decomposition of communication emitter symbol waveform signals is improved with whale optimization algorithm, in which the sequence complexity is used as the stopping criterion in this method to enable each symbol waveform signal to adaptively decompose several high-frequency signal components containing nonlinear fingerprint features and low-frequency components of data information; Then, according to the relevant threshold, the number of high-frequency signal component layers is selected that can best represent the nonlinear characteristics of the radiation source and the fuzzy entropy, permutation entropy, Higuchi dimension, and Katz dimension are extracted to form a multi-domain joint feature vector; Finally, the recognition and classification of communication emitters are achieved through convolutional neural networks, and recognition and classification experiments are conducted using the Oracle public dataset. The experimental results show that this method has high recognition accuracy and good noise immunity.
- Specific emitter identification,
- Variational modal decomposition,
- Nonlinear fingerprint feature,
- Convolutional neural network

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References(19)

References

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