Bilateral‐Branch Signal Feature Enhancement Network for RFF Data Enhancement

Abstract

The clarity of radio frequency fingerprint (RFF) features is an important factor affecting the recognition effect of the classifier. Recognition of RFF signals is essential in radio frequency (RF) perception. However, the RF signal is easily affected by interference and noise during transmission, which makes the distribution boundary of the characteristics of the RFF unclear. It then affects the recognition effect of the classifier. The common preprocessing algorithms, such as denoising and channel compensation, can improve the performance of communication demodulation but seriously affect the unique signal distortion of RFF characteristics. The spectral overlap between the useful signal and the noise is serious when the spectrum is broad, so it is difficult to separate the signal from the noise by traditional methods effectively. Therefore, this paper proposes a data enhancement technology for RF signals named random railings to increase the diversity of input samples and improve the overall performance of the model. At the same time, this paper also proposes a bilateral‐branch signal feature enhancement network. The dual branch structure of BBSEN extracts the features of the enhanced time‐domain signal and the spectrum obtained by the short‐time Fourier transform (STFT) of the signal and then achieves feature fusion through the improved self‐attention mechanism to obtain the RFF features, which are more suitable for the interference environment. Experiments show that the algorithm proposed in this paper can effectively improve the antijamming environment of RF signals and even can be used after preprocessing algorithms such as denoising and further improve the recognition rate of high signal‐to‐noise ratio signals.