Abstract
In the wake of the development and advancement of signal processing technology for communication radiation source individual, Signal fingerprint feature extraction and analysis technology for communication radiation source individual has broad application prospects in many fields. To effectively extract the individual characteristics of different modulated signals under low SNR environment, and recognize the subtle features for communication radiation source individuals has been a hot spot. Aiming at the problem of signal feature extraction and classifier design under low SNR environment, in the paper, a multifractal dimension and improved gray relation theory based classifier design algorithm is proposed. Firstly, the multifractal dimension feature extraction of nine modulated communication signals is realized. Then multifractal dimension features of these modulated signals under different SNR are compared. An improved gray relation algorithm is used to recognize the extracted subtle characteristics. Meanwhile, FSK signal is used to simulate radio subtle features by adding different distribution of noise. Subtle feature extraction by means of multifractal dimension algorithm and pattern recognition by means of improved gray relation algorithm are used to test the effectiveness of the proposed method for identification of modulated signals and radio subtle features. The simulation results show that the recognition success rate of nine different communication modulated signals can reach 93% even under the SNR of 2 dB, and the recognition success rate of the subtle features of distributed noise can reach 100%. The proposed method provides an effective theoretical basis for identifying of radio modulated signals and communication radiation source individual subtle features.
Highlights
Under the background of the increasingly widespread application of communication technology disciplines and the rapid development of signal processing disciplines, the identification of communication radiation source individuals has gradually attracted the researchers attention [1, 2]
Subtle feature extraction by means of multifractal dimension algorithm and pattern recognition by means of improved gray relation algorithm are used to test the effectiveness of the proposed method for identification of modulated signals and radio subtle features
The multifractal dimension algorithm can describe the complexity and irregularity of the signal compared with fractal-box dimension algorithm [19]
Summary
Under the background of the increasingly widespread application of communication technology disciplines and the rapid development of signal processing disciplines, the identification of communication radiation source individuals has gradually attracted the researchers attention [1, 2]. The literature [23,24,25] summarizes the application of fractals in heat transfer and mass transfer of media, the application in geomorphology, and the application in manufacturing systems It can be seen from the literatures in recent years that fractal dimension has achieved good development in various fields. The main contributions of this paper are as follows: first, extract the multifractal dimension features of nine modulated signals under different SNR environment, the features of FSK signals with different distributed noise are extracted, at last, an improved adaptive interval gray relation algorithm [29, 30] is used to classify the extracted characteristics, and the purpose of accurately identifying signals is achieved
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