Abstract
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.
Highlights
Specific Emitter Identification (SEI) is the process of identifying individual emitters by matching the characteristics of the received signal with the emitter for correlation [1]
SEI technology is the fusion of signal processing technology and pattern recognition technology, which can be divided into three parts: data preprocessing, analysis of subtle features, and design of the classifier
The structure of this paper is composed as follows: in Section 2, the time–frequency analysis method and neural network model utilized in this paper are introduced; in Section 3, the classification method combining multi-domain features and integrated learning is described in detail; in Section 4, the identification results and discussion for both fixed-frequency and frequency-hopping sample sets are presented; and in Section 5, the conclusions are given
Summary
Specific Emitter Identification (SEI) is the process of identifying individual emitters by matching the characteristics of the received signal with the emitter for correlation [1]. The analysis results of the measured data prove that the proposed method has a higher accuracy rate as compared with the classical feature extraction method, and can improve the identification of communication radiation sources when the labeled samples are small. The structure of this paper is composed as follows: in Section 2, the time–frequency analysis method and neural network model utilized in this paper are introduced; in Section 3, the classification method combining multi-domain features and integrated learning is described in detail; in Section 4, the identification results and discussion for both fixed-frequency and frequency-hopping sample sets are presented; and, the conclusions are given The structure of this paper is composed as follows: in Section 2, the time–frequency analysis method and neural network model utilized in this paper are introduced; in Section 3, the classification method combining multi-domain features and integrated learning is described in detail; in Section 4, the identification results and discussion for both fixed-frequency and frequency-hopping sample sets are presented; and in Section 5, the conclusions are given
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