Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning

Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources accordin...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-08, Vol.13 (8), p.1481
Main Authors: Qu, Ling-Zhi, Liu, Hui, Huang, Ke-Ju, Yang, Jun-An
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Communication
Datasets
Deep learning
Emitters
Feature extraction
Frequency distribution
Identification
integrated learning
Learning
less labeled samples
multi-domain features
Neural networks
Radiation
Radiation sources
Signal processing
specific emitter identification
Time domain analysis
Wavelet transforms
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Summary:Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13081481